ABCA1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.259 0.772 0.226 0.496 1.201 -1.148 0.251 Age 0.005 1.005 0.014 0.978 1.033 0.386 0.700 Gendermale 0.322 1.380 0.419 0.607 3.136 0.769 0.442 RaceBlack -0.496 0.609 12219.090 0.000 Inf 0.000 1.000 RaceWhite 16.323 12276063.195 10428.582 0.000 Inf 0.002 0.999 Purity 2.953 19.165 2.305 0.209 1755.891 1.281 0.200 Rsquare = 0.086 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.5e-01 Wald test p = 7.28e-01 Score (logrank) test p = 5.54e-01 ABCA1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.030 1.030 0.100 0.847 1.254 0.300 0.764 Age 0.034 1.034 0.009 1.017 1.052 3.894 0.000 *** Gendermale -0.171 0.843 0.178 0.594 1.195 -0.959 0.338 RaceBlack 0.702 2.018 0.447 0.841 4.845 1.572 0.116 RaceWhite 0.110 1.116 0.355 0.556 2.238 0.309 0.757 Stage2 14.563 2111186.957 1858.427 0.000 Inf 0.008 0.994 Stage3 14.994 3249649.987 1858.427 0.000 Inf 0.008 0.994 Stage4 15.541 5613301.305 1858.427 0.000 Inf 0.008 0.993 Purity 0.156 1.169 0.342 0.598 2.286 0.455 0.649 Rsquare = 0.13 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.95e-07 Wald test p = 1.23e-06 Score (logrank) test p = 3.33e-07 ABCA1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.396 1.485 0.115 1.185 1.863 3.428 0.001 ** Age 0.038 1.039 0.008 1.024 1.055 5.109 0.000 *** Gendermale -0.099 0.906 1.009 0.125 6.546 -0.098 0.922 RaceBlack 0.037 1.038 0.620 0.308 3.502 0.060 0.952 RaceWhite -0.288 0.750 0.597 0.233 2.415 -0.483 0.629 Stage2 0.474 1.607 0.305 0.883 2.923 1.552 0.121 Stage3 1.267 3.550 0.315 1.914 6.585 4.019 0.000 *** Stage4 2.760 15.797 0.396 7.262 34.361 6.960 0.000 *** Purity 0.897 2.452 0.435 1.045 5.754 2.061 0.039 * Rsquare = 0.093 (max possible = 7.85e-01 ) Likelihood ratio test p = 1.11e-14 Wald test p = 8.91e-18 Score (logrank) test p = 9.43e-24 ABCA1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.409 1.505000e+00 0.250 0.921 2.459 1.633 0.102 Age 0.002 1.002000e+00 0.019 0.967 1.040 0.134 0.893 RaceBlack -0.810 4.450000e-01 1.105 0.051 3.879 -0.733 0.463 RaceWhite -1.287 2.760000e-01 1.104 0.032 2.403 -1.166 0.244 Stage2 18.791 1.447895e+08 6386.339 0.000 Inf 0.003 0.998 Stage3 20.292 6.494833e+08 6386.339 0.000 Inf 0.003 0.997 Stage4 21.436 2.039148e+09 6386.339 0.000 Inf 0.003 0.997 Purity 1.085 2.958000e+00 0.957 0.454 19.287 1.134 0.257 Rsquare = 0.171 (max possible = 7.18e-01 ) Likelihood ratio test p = 1.72e-04 Wald test p = 2.58e-03 Score (logrank) test p = 1.54e-06 ABCA1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.363 1.438000e+00 0.523 0.516 4.004 0.694 0.487 Age 0.042 1.043000e+00 0.032 0.980 1.110 1.315 0.189 RaceBlack -2.591 7.500000e-02 1.915 0.002 3.195 -1.353 0.176 RaceWhite -1.471 2.300000e-01 1.488 0.012 4.245 -0.988 0.323 Stage2 18.200 8.016481e+07 13960.044 0.000 Inf 0.001 0.999 Stage3 19.621 3.322268e+08 13960.044 0.000 Inf 0.001 0.999 Stage4 52.690 7.639585e+22 1973857.385 0.000 Inf 0.000 1.000 Purity 3.145 2.322800e+01 2.279 0.267 2023.826 1.380 0.168 Rsquare = 0.375 (max possible = 6.68e-01 ) Likelihood ratio test p = 5.23e-04 Wald test p = 1e+00 Score (logrank) test p = 3.64e-14 ABCA1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.563 1.756 0.181 1.232 2.501 3.117 0.002 ** Age 0.052 1.053 0.012 1.029 1.077 4.477 0.000 *** Gendermale -16.040 0.000 3445.989 0.000 Inf -0.005 0.996 RaceBlack -0.365 0.694 1.178 0.069 6.979 -0.310 0.757 RaceWhite 0.083 1.086 1.036 0.143 8.269 0.080 0.936 Stage2 0.386 1.472 0.376 0.705 3.074 1.028 0.304 Stage3 1.059 2.885 0.402 1.312 6.343 2.635 0.008 ** Stage4 2.398 10.997 0.599 3.398 35.590 4.001 0.000 *** Purity 0.933 2.542 0.651 0.709 9.112 1.433 0.152 Rsquare = 0.089 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.92e-06 Wald test p = 7.99e-07 Score (logrank) test p = 1.14e-08 ABCA1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.580 1.787 0.322 0.951 3.357 1.803 0.071 · Age 0.058 1.060 0.022 1.014 1.108 2.590 0.010 * Gendermale 1.470 4.347 1.144 0.462 40.946 1.284 0.199 RaceBlack 16.832 20428471.382 6836.548 0.000 Inf 0.002 0.998 RaceWhite 16.016 9027496.948 6836.548 0.000 Inf 0.002 0.998 Stage2 0.865 2.374 1.078 0.287 19.619 0.802 0.422 Stage3 1.800 6.050 1.068 0.745 49.118 1.685 0.092 · Stage4 2.802 16.480 1.254 1.412 192.328 2.235 0.025 * Purity 2.336 10.340 1.509 0.537 199.190 1.548 0.122 Rsquare = 0.124 (max possible = 6.98e-01 ) Likelihood ratio test p = 1.39e-02 Wald test p = 3.37e-02 Score (logrank) test p = 8.49e-03 ABCA1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.042 0.959 0.161 0.699 1.316 -0.260 0.794 Age 0.012 1.012 0.010 0.992 1.032 1.152 0.249 RaceBlack 1.008 2.740 1.078 0.331 22.678 0.935 0.350 RaceWhite 0.802 2.229 1.018 0.303 16.387 0.788 0.431 Purity 0.512 1.669 0.768 0.371 7.519 0.667 0.505 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.69e-01 Wald test p = 7.06e-01 Score (logrank) test p = 6.97e-01 ABCA1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.266 0.766 0.415 0.340 1.727 -0.642 0.521 Age 0.016 1.016 0.023 0.972 1.062 0.703 0.482 Gendermale 0.287 1.332 0.569 0.437 4.062 0.504 0.614 RaceBlack -0.018 0.982 1.575 0.045 21.524 -0.011 0.991 RaceWhite -1.131 0.323 0.909 0.054 1.918 -1.244 0.213 Stage2 0.730 2.076 0.696 0.531 8.118 1.050 0.294 Stage3 -15.886 0.000 6945.669 0.000 Inf -0.002 0.998 Stage4 0.781 2.183 0.678 0.578 8.243 1.152 0.249 Purity 1.674 5.333 1.695 0.192 147.848 0.988 0.323 Rsquare = 0.221 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.39e-01 Wald test p = 6.51e-01 Score (logrank) test p = 4.7e-01 ABCA1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.021 0.979 0.167 0.705 1.358 -0.128 0.898 Age 0.024 1.024 0.012 1.001 1.047 2.053 0.040 * Gendermale 0.210 1.233 0.270 0.726 2.095 0.776 0.438 RaceBlack -0.403 0.668 0.834 0.130 3.426 -0.483 0.629 RaceWhite -0.428 0.652 0.785 0.140 3.035 -0.545 0.586 Stage2 0.207 1.230 0.563 0.408 3.707 0.368 0.713 Stage3 0.806 2.238 0.549 0.763 6.569 1.467 0.142 Stage4 1.885 6.584 0.553 2.229 19.451 3.410 0.001 ** Purity -0.248 0.780 0.633 0.226 2.698 -0.392 0.695 Rsquare = 0.109 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.62e-04 Wald test p = 1.59e-04 Score (logrank) test p = 2.32e-05 ABCA1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 1.134 3.107 0.591 0.976 9.893 1.919 0.055 · Age -0.009 0.991 0.049 0.900 1.091 -0.180 0.857 Gendermale -0.130 0.878 1.227 0.079 9.734 -0.106 0.916 RaceBlack 1.335 3.799 1.967 0.080 179.579 0.678 0.497 RaceWhite -3.162 0.042 1.611 0.002 0.995 -1.963 0.050 · Purity -1.857 0.156 2.889 0.001 44.905 -0.643 0.520 Rsquare = 0.221 (max possible = 5.58e-01 ) Likelihood ratio test p = 1.15e-01 Wald test p = 2.53e-01 Score (logrank) test p = 9.41e-02 ABCA1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.071 1.073 0.172 0.766 1.505 0.410 0.682 Age 0.010 1.011 0.014 0.983 1.039 0.737 0.461 Gendermale 0.501 1.650 0.539 0.573 4.748 0.928 0.353 RaceBlack 0.385 1.469 1.075 0.179 12.074 0.358 0.720 RaceWhite -0.032 0.968 0.462 0.391 2.395 -0.070 0.944 Stage2 0.674 1.961 0.656 0.542 7.098 1.026 0.305 Stage3 1.422 4.146 0.675 1.103 15.577 2.106 0.035 * Stage4 2.801 16.462 0.788 3.515 77.102 3.555 0.000 *** Purity 0.365 1.440 0.858 0.268 7.744 0.425 0.671 Rsquare = 0.142 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.09e-02 Wald test p = 5.09e-03 Score (logrank) test p = 4.28e-04 ABCA1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.016 0.984 0.118 0.781 1.240 -0.134 0.893 Age 0.030 1.030 0.009 1.013 1.048 3.519 0.000 *** Gendermale -0.099 0.906 0.215 0.594 1.380 -0.461 0.645 RaceBlack 0.514 1.672 0.733 0.397 7.040 0.701 0.483 RaceWhite -0.254 0.776 0.622 0.229 2.628 -0.408 0.683 Purity -1.103 0.332 0.547 0.114 0.970 -2.016 0.044 * Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.77e-03 Wald test p = 6.95e-03 Score (logrank) test p = 6e-03 ABCA1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.005 0.995 0.079 0.852 1.162 -0.068 0.946 Age 0.022 1.022 0.008 1.007 1.038 2.902 0.004 ** Gendermale -0.249 0.780 0.172 0.557 1.093 -1.444 0.149 RaceBlack 0.133 1.143 0.559 0.382 3.416 0.238 0.812 RaceWhite -0.248 0.780 0.511 0.286 2.125 -0.486 0.627 Stage2 0.615 1.849 0.544 0.637 5.373 1.130 0.258 Stage3 0.847 2.332 0.540 0.809 6.717 1.568 0.117 Stage4 1.252 3.498 0.511 1.285 9.523 2.451 0.014 * Purity -0.049 0.952 0.366 0.465 1.951 -0.134 0.893 Rsquare = 0.069 (max possible = 9.89e-01 ) Likelihood ratio test p = 5.26e-04 Wald test p = 1.39e-03 Score (logrank) test p = 1.02e-03 ABCA1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.155 8.560000e-01 0.226 0.550 1.332 -0.688 0.492 Age 0.011 1.011000e+00 0.026 0.962 1.063 0.431 0.666 Gendermale -0.129 8.790000e-01 0.545 0.302 2.558 -0.237 0.812 RaceBlack 18.553 1.141830e+08 12106.732 0.000 Inf 0.002 0.999 RaceWhite 17.874 5.789105e+07 12106.732 0.000 Inf 0.001 0.999 Stage2 17.467 3.854461e+07 5238.906 0.000 Inf 0.003 0.997 Stage3 16.536 1.518967e+07 5238.906 0.000 Inf 0.003 0.997 Stage4 17.381 3.535209e+07 5238.906 0.000 Inf 0.003 0.997 Purity -1.642 1.940000e-01 1.049 0.025 1.514 -1.565 0.118 Rsquare = 0.094 (max possible = 9.17e-01 ) Likelihood ratio test p = 7e-01 Wald test p = 9.19e-01 Score (logrank) test p = 8.17e-01 ABCA1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.018 1.018 0.085 0.861 1.204 0.214 0.830 Age 0.027 1.027 0.008 1.010 1.044 3.196 0.001 ** Gendermale -0.287 0.751 0.183 0.525 1.074 -1.568 0.117 RaceBlack -0.017 0.983 0.564 0.325 2.968 -0.031 0.975 RaceWhite -0.393 0.675 0.512 0.247 1.843 -0.767 0.443 Stage2 0.372 1.450 0.554 0.489 4.297 0.671 0.502 Stage3 0.740 2.096 0.544 0.721 6.094 1.359 0.174 Stage4 1.154 3.171 0.513 1.160 8.669 2.250 0.024 * Purity 0.219 1.244 0.402 0.566 2.734 0.545 0.586 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.34e-04 Wald test p = 9.46e-04 Score (logrank) test p = 7.11e-04 ABCA1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p ABCA1 0.368 1.446000e+00 0.400 0.659 3.169000e+00 0.920 0.357 Age 0.078 1.081000e+00 0.029 1.022 1.144000e+00 2.725 0.006 Gendermale -0.660 5.170000e-01 0.730 0.124 2.159000e+00 -0.905 0.365 RaceBlack -17.017 0.000000e+00 6214.933 0.000 Inf -0.003 0.998 RaceWhite -1.602 2.010000e-01 1.162 0.021 1.965000e+00 -1.379 0.168 Stage2 16.047 9.314782e+06 0.852 1754773.628 4.944522e+07 18.842 0.000 Stage3 16.986 2.381433e+07 0.775 5212799.735 1.087941e+08 21.914 0.000 Stage4 19.237 2.261898e+08 0.902 38606657.409 1.325207e+09 21.326 0.000 Purity 1.272 3.569000e+00 3.469 0.004 3.203610e+03 0.367 0.714 signif ABCA1 Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.352 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.23e-03 Wald test p = 1.72e-274 Score (logrank) test p = 9.25e-09 ABCA1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.165 0.848 0.103 0.693 1.038 -1.595 0.111 Age 0.035 1.035 0.008 1.018 1.053 4.098 0.000 *** Gendermale -0.104 0.901 0.184 0.629 1.292 -0.567 0.571 RaceBlack -0.017 0.983 1.064 0.122 7.912 -0.016 0.987 RaceWhite 0.027 1.027 1.017 0.140 7.532 0.026 0.979 Stage2 0.181 1.198 0.345 0.609 2.357 0.523 0.601 Stage3 0.809 2.245 0.230 1.431 3.522 3.519 0.000 *** Stage4 1.723 5.600 0.216 3.666 8.556 7.969 0.000 *** Purity -0.050 0.952 0.370 0.461 1.965 -0.134 0.893 Rsquare = 0.178 (max possible = 9.65e-01 ) Likelihood ratio test p = 3.29e-15 Wald test p = 3.14e-15 Score (logrank) test p = 1.72e-18 ABCA1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.140 0.870 0.210 0.576 1.312 -0.666 0.506 Age 0.005 1.006 0.016 0.975 1.037 0.349 0.727 Gendermale -0.488 0.614 0.383 0.290 1.300 -1.275 0.202 RaceBlack -2.011 0.134 1.191 0.013 1.381 -1.689 0.091 · RaceWhite -2.051 0.129 1.172 0.013 1.280 -1.749 0.080 · Stage2 -0.405 0.667 1.054 0.084 5.264 -0.385 0.701 Stage3 1.618 5.044 0.425 2.192 11.609 3.805 0.000 *** Stage4 2.686 14.668 0.506 5.441 39.537 5.308 0.000 *** Purity -0.298 0.742 0.756 0.168 3.269 -0.394 0.693 Rsquare = 0.165 (max possible = 7.58e-01 ) Likelihood ratio test p = 9.64e-06 Wald test p = 2.55e-06 Score (logrank) test p = 4.25e-10 ABCA1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.196 0.822 0.093 0.685 0.987 -2.098 0.036 * Age 0.039 1.040 0.008 1.023 1.057 4.760 0.000 *** Gendermale -0.082 0.921 0.213 0.607 1.400 -0.383 0.701 RaceBlack -0.565 0.568 1.110 0.065 5.003 -0.509 0.611 RaceWhite -0.975 0.377 1.027 0.050 2.824 -0.949 0.342 Rsquare = 0.18 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.76e-05 Wald test p = 7.29e-05 Score (logrank) test p = 5.57e-05 ABCA1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.207 1.230 0.109 0.994 1.522 1.904 0.057 · Age 0.063 1.065 0.008 1.049 1.082 8.177 0.000 *** Gendermale 0.059 1.061 0.195 0.725 1.554 0.305 0.761 RaceBlack 15.058 3464562.109 2157.897 0.000 Inf 0.007 0.994 RaceWhite 15.174 3892040.904 2157.897 0.000 Inf 0.007 0.994 Purity -1.040 0.353 0.407 0.159 0.785 -2.554 0.011 * Rsquare = 0.143 (max possible = 9.07e-01 ) Likelihood ratio test p = 2.02e-13 Wald test p = 1.94e-13 Score (logrank) test p = 1.5e-14 ABCA1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.086 1.090 0.104 0.888 1.338 0.828 0.408 Age 0.012 1.012 0.008 0.996 1.028 1.460 0.144 Gendermale -0.131 0.877 0.226 0.563 1.366 -0.580 0.562 RaceBlack 0.899 2.457 0.489 0.941 6.413 1.837 0.066 · RaceWhite -0.008 0.992 0.237 0.623 1.579 -0.033 0.974 Stage2 0.306 1.358 0.261 0.814 2.266 1.173 0.241 Stage3 0.934 2.546 0.235 1.605 4.039 3.969 0.000 *** Stage4 1.595 4.930 0.619 1.466 16.579 2.578 0.010 * Purity 0.610 1.840 0.459 0.748 4.524 1.328 0.184 Rsquare = 0.087 (max possible = 9.66e-01 ) Likelihood ratio test p = 9.27e-04 Wald test p = 5.52e-04 Score (logrank) test p = 2.07e-04 ABCA1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.057 0.944 0.089 0.792 1.125 -0.641 0.522 Age 0.007 1.007 0.009 0.989 1.025 0.745 0.456 Gendermale 0.022 1.022 0.169 0.734 1.424 0.129 0.897 RaceBlack 16.071 9542205.963 1877.857 0.000 Inf 0.009 0.993 RaceWhite 16.266 11588305.410 1877.857 0.000 Inf 0.009 0.993 Stage2 0.862 2.369 0.201 1.597 3.512 4.290 0.000 *** Stage3 1.019 2.771 0.218 1.807 4.248 4.675 0.000 *** Stage4 0.999 2.715 0.334 1.410 5.225 2.989 0.003 ** Purity 0.542 1.719 0.353 0.861 3.430 1.536 0.125 Rsquare = 0.097 (max possible = 9.74e-01 ) Likelihood ratio test p = 2.02e-06 Wald test p = 2.6e-05 Score (logrank) test p = 3.09e-06 ABCA1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.061 1.063 0.095 0.883 1.280 0.644 0.520 Age 0.016 1.017 0.009 0.998 1.035 1.759 0.079 · Gendermale 0.438 1.550 0.194 1.061 2.266 2.265 0.024 * RaceBlack -0.024 0.976 0.610 0.295 3.228 -0.040 0.968 RaceWhite -0.531 0.588 0.565 0.194 1.780 -0.940 0.347 Stage2 0.205 1.227 0.187 0.851 1.770 1.095 0.273 Stage3 0.608 1.836 0.215 1.206 2.796 2.833 0.005 ** Stage4 0.702 2.017 0.802 0.419 9.722 0.875 0.382 Purity -0.302 0.739 0.371 0.357 1.531 -0.813 0.416 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.1e-02 Wald test p = 1.66e-02 Score (logrank) test p = 1.41e-02 ABCA1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.080 1.083 0.169 0.777 1.509 0.470 0.639 Age 0.021 1.021 0.016 0.990 1.054 1.322 0.186 Gendermale -0.204 0.816 0.334 0.424 1.569 -0.610 0.542 RaceBlack 0.031 1.031 1.545 0.050 21.311 0.020 0.984 RaceWhite -0.558 0.572 1.051 0.073 4.495 -0.531 0.596 Stage2 -0.238 0.789 0.467 0.316 1.969 -0.509 0.611 Stage3 -0.092 0.912 0.417 0.403 2.065 -0.221 0.825 Stage4 -0.168 0.845 0.477 0.332 2.154 -0.352 0.725 Purity -0.667 0.513 0.581 0.164 1.604 -1.147 0.251 Rsquare = 0.062 (max possible = 9.98e-01 ) Likelihood ratio test p = 7.91e-01 Wald test p = 7.72e-01 Score (logrank) test p = 7.65e-01 ABCA1 in OV (n=303): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.193 1.213 0.119 0.960 1.531 1.620 0.105 Age 0.035 1.035 0.008 1.019 1.052 4.214 0.000 *** RaceBlack -0.113 0.893 0.577 0.288 2.768 -0.196 0.844 RaceWhite -0.160 0.852 0.515 0.310 2.338 -0.311 0.756 Purity -0.369 0.691 0.678 0.183 2.611 -0.544 0.586 Rsquare = 0.091 (max possible = 9.97e-01 ) Likelihood ratio test p = 3.65e-04 Wald test p = 3.67e-04 Score (logrank) test p = 2.97e-04 ABCA1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.071 0.931 0.141 0.706 1.228 -0.505 0.613 Age 0.021 1.021 0.011 0.999 1.043 1.856 0.063 · Gendermale -0.238 0.788 0.221 0.511 1.216 -1.075 0.282 RaceBlack 0.003 1.003 0.740 0.235 4.273 0.004 0.997 RaceWhite 0.360 1.433 0.473 0.567 3.622 0.761 0.447 Stage2 0.610 1.840 0.438 0.780 4.337 1.393 0.164 Stage3 -0.265 0.767 1.092 0.090 6.525 -0.243 0.808 Stage4 0.246 1.279 0.824 0.254 6.435 0.299 0.765 Purity -0.754 0.470 0.443 0.197 1.120 -1.703 0.089 · Rsquare = 0.09 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.71e-02 Wald test p = 1.15e-01 Score (logrank) test p = 1.1e-01 ABCA1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 1.008 2.740 0.511 1.007 7.457 1.974 0.048 * Age 0.051 1.052 0.027 0.997 1.110 1.850 0.064 · Gendermale 1.848 6.349 0.987 0.918 43.919 1.873 0.061 · RaceBlack -1.217 0.296 33065.202 0.000 Inf 0.000 1.000 RaceWhite 17.825 55129058.296 29109.046 0.000 Inf 0.001 1.000 Purity 8.230 3753.558 3.871 1.904 7400942.121 2.126 0.033 * Rsquare = 0.077 (max possible = 3.07e-01 ) Likelihood ratio test p = 3.96e-02 Wald test p = 2.82e-01 Score (logrank) test p = 1.62e-01 ABCA1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.750 2.117 0.451 0.875 5.121 1.665 0.096 · Age 0.022 1.022 0.056 0.916 1.141 0.393 0.694 RaceBlack 15.911 8130825.437 11001.767 0.000 Inf 0.001 0.999 RaceWhite 17.192 29277873.551 11001.767 0.000 Inf 0.002 0.999 Purity 1.497 4.467 1.397 0.289 69.016 1.072 0.284 Rsquare = 0.013 (max possible = 1.83e-01 ) Likelihood ratio test p = 3.75e-01 Wald test p = 4.57e-01 Score (logrank) test p = 4.02e-01 ABCA1 in READ (n=166): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.681 1.975 0.438 0.837 4.664 1.553 0.120 Age 0.123 1.131 0.048 1.030 1.242 2.575 0.010 * Gendermale -0.336 0.714 0.730 0.171 2.986 -0.461 0.645 RaceBlack 13.489 721715.916 10565.196 0.000 Inf 0.001 0.999 RaceWhite 12.076 175526.456 10565.196 0.000 Inf 0.001 0.999 Stage2 -2.349 0.095 1.296 0.008 1.210 -1.813 0.070 · Stage3 -0.786 0.456 0.953 0.070 2.950 -0.825 0.409 Stage4 -0.508 0.602 0.990 0.086 4.189 -0.513 0.608 Purity 0.573 1.773 1.474 0.099 31.889 0.388 0.698 Rsquare = 0.233 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.71e-02 Wald test p = 1.66e-01 Score (logrank) test p = 3.2e-02 ABCA1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.165 0.848 0.115 0.678 1.062 -1.436 0.151 Age 0.023 1.023 0.008 1.007 1.040 2.785 0.005 ** Gendermale -0.018 0.982 0.223 0.634 1.520 -0.081 0.935 RaceBlack -0.148 0.863 1.086 0.103 7.249 -0.136 0.892 RaceWhite -0.519 0.595 1.024 0.080 4.427 -0.507 0.612 Purity 0.627 1.872 0.600 0.577 6.069 1.044 0.296 Rsquare = 0.051 (max possible = 9.75e-01 ) Likelihood ratio test p = 5.71e-02 Wald test p = 7.13e-02 Score (logrank) test p = 7.04e-02 ABCA1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.164 0.849 0.078 0.728 0.989 -2.101 0.036 * Age 0.017 1.018 0.005 1.007 1.028 3.343 0.001 ** Gendermale -0.032 0.969 0.158 0.711 1.321 -0.200 0.842 RaceWhite -1.285 0.277 0.402 0.126 0.608 -3.201 0.001 ** Stage2 0.299 1.348 0.219 0.877 2.071 1.362 0.173 Stage3 0.665 1.945 0.206 1.298 2.913 3.225 0.001 ** Stage4 1.443 4.235 0.354 2.115 8.478 4.075 0.000 *** Purity 0.780 2.181 0.356 1.085 4.384 2.188 0.029 * Rsquare = 0.133 (max possible = 9.92e-01 ) Likelihood ratio test p = 3.09e-09 Wald test p = 1.43e-09 Score (logrank) test p = 1.63e-10 ABCA1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.219 8.040000e-01 0.216 0.526 1.227 -1.013 0.311 Age 0.010 1.010000e+00 0.016 0.978 1.043 0.615 0.539 Gendermale 0.267 1.306000e+00 0.434 0.559 3.055 0.616 0.538 RaceWhite -1.289 2.750000e-01 0.619 0.082 0.927 -2.083 0.037 * Stage2 17.400 3.602974e+07 6228.856 0.000 Inf 0.003 0.998 Stage3 17.872 5.774376e+07 6228.856 0.000 Inf 0.003 0.998 Stage4 20.230 6.103432e+08 6228.856 0.000 Inf 0.003 0.997 Purity 0.353 1.423000e+00 0.951 0.221 9.188 0.371 0.711 Rsquare = 0.156 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.36e-02 Wald test p = 4.14e-02 Score (logrank) test p = 3.18e-03 ABCA1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.152 0.859 0.085 0.728 1.014 -1.796 0.073 · Age 0.020 1.020 0.006 1.009 1.031 3.475 0.001 ** Gendermale -0.046 0.955 0.173 0.681 1.340 -0.266 0.790 RaceWhite -1.046 0.351 0.600 0.108 1.139 -1.743 0.081 · Stage2 0.172 1.188 0.231 0.755 1.869 0.746 0.456 Stage3 0.617 1.853 0.212 1.224 2.806 2.916 0.004 ** Stage4 1.216 3.373 0.402 1.535 7.413 3.027 0.002 ** Purity 0.909 2.482 0.390 1.156 5.329 2.333 0.020 * Rsquare = 0.143 (max possible = 9.95e-01 ) Likelihood ratio test p = 2.68e-07 Wald test p = 3.42e-07 Score (logrank) test p = 1.33e-07 ABCA1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.262 1.299 0.108 1.051 1.606 2.422 0.015 * Age 0.028 1.029 0.010 1.008 1.050 2.781 0.005 ** Gendermale 0.132 1.141 0.208 0.758 1.716 0.632 0.528 RaceBlack 0.341 1.406 0.449 0.583 3.389 0.759 0.448 RaceWhite 0.044 1.045 0.245 0.646 1.689 0.178 0.859 Stage2 0.466 1.594 0.389 0.744 3.414 1.200 0.230 Stage3 0.831 2.296 0.363 1.126 4.681 2.287 0.022 * Stage4 1.391 4.020 0.502 1.504 10.745 2.774 0.006 ** Purity -0.423 0.655 0.378 0.312 1.373 -1.120 0.263 Rsquare = 0.088 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.59e-03 Wald test p = 2.13e-03 Score (logrank) test p = 1.47e-03 ABCA1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 14.988 3.228589e+06 31840.162 0 Inf 0.000 1.000 Age -1.755 1.730000e-01 1679.356 0 Inf -0.001 0.999 RaceBlack 1.029 2.799000e+00 14555420.565 0 Inf 0.000 1.000 RaceWhite -39.769 0.000000e+00 14864235.836 0 Inf 0.000 1.000 Stage2 -21.955 0.000000e+00 43567.796 0 Inf -0.001 1.000 Stage3 20.310 6.615794e+08 127498.786 0 Inf 0.000 1.000 Purity 11.329 8.320711e+04 231578.553 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.22e-03 ABCA1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.371 1.449 0.398 0.664 3.163 0.932 0.351 Age 0.154 1.166 0.030 1.100 1.237 5.148 0.000 *** Gendermale -0.310 0.734 0.697 0.187 2.876 -0.444 0.657 RaceBlack 17.636 45643278.770 9342.543 0.000 Inf 0.002 0.998 RaceWhite 17.321 33289712.352 9342.543 0.000 Inf 0.002 0.999 Stage2 0.229 1.258 1.174 0.126 12.548 0.195 0.845 Stage3 0.455 1.576 0.897 0.272 9.149 0.507 0.612 Stage4 1.988 7.299 1.075 0.887 60.056 1.849 0.065 · Purity 2.209 9.111 1.071 1.116 74.345 2.063 0.039 * Rsquare = 0.151 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.7e-10 Wald test p = 3.3e-04 Score (logrank) test p = 1.19e-10 ABCA1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.204 0.816 0.370 0.395 1.684 -0.551 0.582 Age 0.054 1.055 0.033 0.990 1.125 1.639 0.101 Gendermale -0.227 0.797 0.741 0.186 3.409 -0.306 0.760 RaceBlack -16.573 0.000 10227.193 0.000 Inf -0.002 0.999 RaceWhite 0.514 1.672 1.090 0.197 14.164 0.472 0.637 Purity 0.406 1.501 1.101 0.173 12.996 0.369 0.712 Rsquare = 0.047 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.94e-01 Wald test p = 6.41e-01 Score (logrank) test p = 5.45e-01 ABCA1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 0.083 1.086 0.192 0.746 1.582 0.432 0.666 Age 0.050 1.051 0.016 1.019 1.084 3.135 0.002 ** RaceBlack -0.391 0.676 0.796 0.142 3.221 -0.491 0.623 RaceWhite -0.491 0.612 0.751 0.141 2.666 -0.654 0.513 Purity 0.475 1.607 0.651 0.449 5.758 0.729 0.466 Rsquare = 0.039 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.68e-02 Wald test p = 5.73e-02 Score (logrank) test p = 5.57e-02 ABCA1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.016 0.985 0.200 0.665 1.457 -0.078 0.938 Age 0.044 1.045 0.026 0.994 1.099 1.727 0.084 · RaceBlack 17.602 44089019.040 6473.900 0.000 Inf 0.003 0.998 RaceWhite 17.859 57039563.776 6473.900 0.000 Inf 0.003 0.998 Purity -0.872 0.418 1.054 0.053 3.300 -0.827 0.408 Rsquare = 0.119 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.53e-01 Wald test p = 3.62e-01 Score (logrank) test p = 2.66e-01 ABCA1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `ABCA1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ABCA1 -0.046 0.955 0.210 0.633 1.442 -0.217 0.828 Age 0.040 1.041 0.019 1.002 1.081 2.077 0.038 * Gendermale 0.257 1.293 0.490 0.495 3.382 0.525 0.600 Stage3 0.273 1.314 0.506 0.487 3.544 0.539 0.590 Stage4 3.732 41.760 1.223 3.800 458.971 3.051 0.002 ** Purity 1.945 6.997 1.230 0.627 78.028 1.581 0.114 Rsquare = 0.253 (max possible = 8.72e-01 ) Likelihood ratio test p = 9.88e-04 Wald test p = 3.31e-03 Score (logrank) test p = 2.39e-09 ACAT1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.191 0.827 0.204 0.554 1.233 -0.933 0.351 Age 0.004 1.004 0.014 0.978 1.032 0.313 0.754 Gendermale 0.299 1.348 0.423 0.588 3.088 0.706 0.480 RaceBlack -0.024 0.977 12091.159 0.000 Inf 0.000 1.000 RaceWhite 16.580 15863216.107 10306.752 0.000 Inf 0.002 0.999 Purity 2.942 18.958 2.389 0.176 2046.353 1.232 0.218 Rsquare = 0.078 (max possible = 9.38e-01 ) Likelihood ratio test p = 5.16e-01 Wald test p = 8.14e-01 Score (logrank) test p = 6.5e-01 ACAT1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.193 1.212 0.110 0.977 1.505 1.749 0.080 · Age 0.032 1.032 0.009 1.015 1.050 3.711 0.000 *** Gendermale -0.186 0.831 0.179 0.585 1.180 -1.037 0.300 RaceBlack 0.617 1.854 0.449 0.768 4.473 1.373 0.170 RaceWhite 0.021 1.021 0.359 0.505 2.064 0.058 0.954 Stage2 14.452 1889260.707 1865.786 0.000 Inf 0.008 0.994 Stage3 14.876 2886830.698 1865.786 0.000 Inf 0.008 0.994 Stage4 15.409 4921374.885 1865.786 0.000 Inf 0.008 0.993 Purity 0.260 1.297 0.344 0.661 2.547 0.756 0.450 Rsquare = 0.138 (max possible = 9.91e-01 ) Likelihood ratio test p = 5.48e-08 Wald test p = 2.41e-07 Score (logrank) test p = 7.49e-08 ACAT1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.028 1.029 0.137 0.786 1.347 0.207 0.836 Age 0.036 1.036 0.008 1.021 1.052 4.685 0.000 *** Gendermale 0.033 1.034 1.008 0.143 7.456 0.033 0.974 RaceBlack -0.007 0.993 0.620 0.295 3.349 -0.011 0.992 RaceWhite -0.232 0.793 0.598 0.246 2.558 -0.389 0.698 Stage2 0.413 1.511 0.305 0.832 2.745 1.356 0.175 Stage3 1.189 3.283 0.313 1.778 6.062 3.800 0.000 *** Stage4 2.508 12.279 0.390 5.720 26.361 6.434 0.000 *** Purity 0.509 1.663 0.422 0.728 3.801 1.207 0.228 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.44e-12 Wald test p = 6.39e-16 Score (logrank) test p = 8.28e-22 ACAT1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.087 1.091000e+00 0.269 0.644 1.848 0.323 0.746 Age 0.011 1.011000e+00 0.018 0.976 1.046 0.597 0.551 RaceBlack -0.924 3.970000e-01 1.107 0.045 3.478 -0.834 0.404 RaceWhite -1.225 2.940000e-01 1.114 0.033 2.608 -1.099 0.272 Stage2 18.666 1.277918e+08 6484.106 0.000 Inf 0.003 0.998 Stage3 20.094 5.329936e+08 6484.106 0.000 Inf 0.003 0.998 Stage4 21.409 1.986158e+09 6484.106 0.000 Inf 0.003 0.997 Purity 0.749 2.115000e+00 0.955 0.325 13.755 0.784 0.433 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5e-04 Wald test p = 7.03e-03 Score (logrank) test p = 4.61e-06 ACAT1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.551 5.760000e-01 0.783 0.124 2.674 -0.704 0.482 Age 0.033 1.034000e+00 0.028 0.978 1.093 1.186 0.236 RaceBlack -2.767 6.300000e-02 1.820 0.002 2.224 -1.521 0.128 RaceWhite -1.411 2.440000e-01 1.490 0.013 4.526 -0.947 0.344 Stage2 18.568 1.159216e+08 14974.141 0.000 Inf 0.001 0.999 Stage3 20.174 5.776197e+08 14974.141 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.030 2.069400e+01 2.310 0.224 1915.738 1.311 0.190 Rsquare = 0.376 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.37e-04 Wald test p = 2.44e-01 Score (logrank) test p = 1.03e-14 ACAT1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.128 0.880 0.214 0.578 1.338 -0.600 0.549 Age 0.049 1.051 0.012 1.026 1.075 4.136 0.000 *** Gendermale -15.257 0.000 3460.573 0.000 Inf -0.004 0.996 RaceBlack -0.396 0.673 1.175 0.067 6.734 -0.337 0.736 RaceWhite 0.311 1.365 1.036 0.179 10.407 0.300 0.764 Stage2 0.300 1.350 0.378 0.644 2.829 0.795 0.427 Stage3 0.852 2.345 0.394 1.083 5.079 2.161 0.031 * Stage4 2.200 9.021 0.597 2.801 29.053 3.686 0.000 *** Purity 0.336 1.399 0.614 0.420 4.658 0.547 0.584 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 9.02e-05 Wald test p = 2.09e-05 Score (logrank) test p = 3.83e-07 ACAT1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.575 1.777 0.341 0.911 3.466 1.685 0.092 · Age 0.050 1.051 0.021 1.010 1.094 2.422 0.015 * Gendermale 1.167 3.213 1.128 0.352 29.334 1.035 0.301 RaceBlack 16.284 11804423.119 6546.620 0.000 Inf 0.002 0.998 RaceWhite 15.669 6379274.116 6546.620 0.000 Inf 0.002 0.998 Stage2 1.049 2.856 1.130 0.312 26.173 0.928 0.353 Stage3 1.855 6.392 1.103 0.736 55.505 1.682 0.093 · Stage4 2.228 9.282 1.198 0.887 97.155 1.860 0.063 · Purity 1.002 2.725 1.345 0.195 38.069 0.745 0.456 Rsquare = 0.121 (max possible = 6.98e-01 ) Likelihood ratio test p = 1.61e-02 Wald test p = 3.7e-02 Score (logrank) test p = 1.12e-02 ACAT1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.055 1.057 0.189 0.730 1.530 0.294 0.769 Age 0.011 1.011 0.010 0.992 1.031 1.116 0.265 RaceBlack 1.069 2.911 1.070 0.357 23.718 0.998 0.318 RaceWhite 0.832 2.297 1.015 0.314 16.809 0.819 0.413 Purity 0.555 1.741 0.736 0.412 7.365 0.754 0.451 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.66e-01 Wald test p = 7.02e-01 Score (logrank) test p = 6.93e-01 ACAT1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.211 1.235 0.272 0.725 2.104 0.775 0.438 Age 0.023 1.023 0.023 0.978 1.071 0.993 0.321 Gendermale 0.374 1.454 0.571 0.475 4.456 0.655 0.512 RaceBlack -0.197 0.821 1.485 0.045 15.075 -0.133 0.895 RaceWhite -1.259 0.284 0.921 0.047 1.725 -1.368 0.171 Stage2 0.754 2.125 0.668 0.574 7.864 1.129 0.259 Stage3 -15.019 0.000 7075.316 0.000 Inf -0.002 0.998 Stage4 0.777 2.176 0.679 0.575 8.230 1.145 0.252 Purity 2.195 8.979 1.590 0.398 202.732 1.380 0.168 Rsquare = 0.224 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.25e-01 Wald test p = 5.68e-01 Score (logrank) test p = 3.93e-01 ACAT1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.093 0.911 0.199 0.616 1.347 -0.468 0.640 Age 0.023 1.023 0.012 1.001 1.047 2.009 0.045 * Gendermale 0.220 1.246 0.269 0.735 2.110 0.817 0.414 RaceBlack -0.409 0.665 0.827 0.131 3.362 -0.494 0.621 RaceWhite -0.456 0.634 0.775 0.139 2.893 -0.589 0.556 Stage2 0.192 1.212 0.564 0.401 3.659 0.340 0.734 Stage3 0.771 2.162 0.554 0.729 6.405 1.391 0.164 Stage4 1.844 6.321 0.559 2.113 18.915 3.297 0.001 ** Purity -0.251 0.778 0.601 0.240 2.525 -0.418 0.676 Rsquare = 0.11 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.27e-04 Wald test p = 1.51e-04 Score (logrank) test p = 2.19e-05 ACAT1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 1.473 4.363 1.028 0.582 32.696 1.434 0.152 Age -0.027 0.973 0.045 0.892 1.062 -0.608 0.543 Gendermale 0.719 2.053 1.042 0.266 15.835 0.690 0.490 RaceBlack 0.396 1.486 1.790 0.045 49.578 0.221 0.825 RaceWhite -2.674 0.069 1.505 0.004 1.317 -1.777 0.076 · Purity -3.660 0.026 2.540 0.000 3.738 -1.441 0.150 Rsquare = 0.176 (max possible = 5.58e-01 ) Likelihood ratio test p = 2.44e-01 Wald test p = 4.68e-01 Score (logrank) test p = 2.39e-01 ACAT1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.140 0.869 0.236 0.547 1.381 -0.594 0.552 Age 0.011 1.011 0.014 0.983 1.039 0.772 0.440 Gendermale 0.478 1.612 0.538 0.561 4.630 0.888 0.375 RaceBlack 0.269 1.308 1.074 0.159 10.748 0.250 0.802 RaceWhite -0.098 0.907 0.447 0.378 2.177 -0.218 0.827 Stage2 0.623 1.864 0.664 0.507 6.851 0.937 0.349 Stage3 1.428 4.171 0.671 1.120 15.531 2.129 0.033 * Stage4 2.843 17.166 0.772 3.784 77.870 3.685 0.000 *** Purity 0.141 1.151 0.779 0.250 5.299 0.180 0.857 Rsquare = 0.143 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.02e-02 Wald test p = 4.6e-03 Score (logrank) test p = 3.77e-04 ACAT1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.068 0.935 0.275 0.545 1.602 -0.246 0.806 Age 0.029 1.030 0.008 1.013 1.047 3.533 0.000 *** Gendermale -0.095 0.909 0.213 0.599 1.381 -0.446 0.655 RaceBlack 0.518 1.678 0.728 0.403 6.986 0.712 0.477 RaceWhite -0.250 0.779 0.615 0.233 2.603 -0.405 0.685 Purity -1.069 0.343 0.538 0.120 0.986 -1.986 0.047 * Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.69e-03 Wald test p = 6.82e-03 Score (logrank) test p = 5.94e-03 ACAT1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.045 0.956 0.129 0.743 1.231 -0.350 0.727 Age 0.022 1.023 0.008 1.007 1.038 2.920 0.003 ** Gendermale -0.250 0.779 0.172 0.556 1.091 -1.455 0.146 RaceBlack 0.146 1.157 0.560 0.386 3.464 0.260 0.795 RaceWhite -0.249 0.780 0.511 0.287 2.122 -0.487 0.626 Stage2 0.622 1.863 0.544 0.642 5.409 1.144 0.253 Stage3 0.852 2.345 0.537 0.819 6.711 1.588 0.112 Stage4 1.266 3.546 0.511 1.303 9.653 2.477 0.013 * Purity -0.057 0.945 0.365 0.462 1.933 -0.155 0.877 Rsquare = 0.07 (max possible = 9.89e-01 ) Likelihood ratio test p = 5.02e-04 Wald test p = 1.42e-03 Score (logrank) test p = 1.02e-03 ACAT1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.223 1.250000e+00 0.367 0.609 2.564 0.608 0.543 Age 0.010 1.010000e+00 0.025 0.961 1.062 0.388 0.698 Gendermale -0.198 8.210000e-01 0.550 0.279 2.414 -0.359 0.720 RaceBlack 19.029 1.838109e+08 12075.531 0.000 Inf 0.002 0.999 RaceWhite 18.139 7.544129e+07 12075.531 0.000 Inf 0.002 0.999 Stage2 17.345 3.409038e+07 5274.309 0.000 Inf 0.003 0.997 Stage3 16.421 1.353322e+07 5274.309 0.000 Inf 0.003 0.998 Stage4 17.424 3.689373e+07 5274.309 0.000 Inf 0.003 0.997 Purity -1.581 2.060000e-01 1.057 0.026 1.634 -1.496 0.135 Rsquare = 0.092 (max possible = 9.17e-01 ) Likelihood ratio test p = 7.12e-01 Wald test p = 9.34e-01 Score (logrank) test p = 8.42e-01 ACAT1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.093 0.912 0.139 0.694 1.197 -0.666 0.506 Age 0.028 1.028 0.009 1.011 1.045 3.235 0.001 ** Gendermale -0.287 0.751 0.183 0.525 1.074 -1.571 0.116 RaceBlack 0.007 1.007 0.565 0.333 3.050 0.013 0.989 RaceWhite -0.401 0.669 0.512 0.245 1.827 -0.783 0.433 Stage2 0.372 1.451 0.554 0.490 4.295 0.672 0.502 Stage3 0.721 2.056 0.541 0.712 5.936 1.332 0.183 Stage4 1.171 3.226 0.513 1.180 8.822 2.282 0.022 * Purity 0.193 1.213 0.402 0.551 2.667 0.479 0.632 Rsquare = 0.086 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.86e-04 Wald test p = 9.31e-04 Score (logrank) test p = 6.8e-04 ACAT1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z ACAT1 -0.231 7.930000e-01 0.252 4.850000e-01 1.299000e+00 -0.920 Age 0.061 1.063000e+00 0.029 1.005000e+00 1.124000e+00 2.122 Gendermale -0.735 4.790000e-01 0.734 1.140000e-01 2.019000e+00 -1.002 RaceBlack -17.990 0.000000e+00 11794.334 0.000000e+00 Inf -0.002 RaceWhite -1.563 2.100000e-01 1.174 2.100000e-02 2.093000e+00 -1.331 Stage2 16.836 2.049995e+07 0.851 3.865407e+06 1.087202e+08 19.779 Stage3 18.174 7.817056e+07 0.779 1.697167e+07 3.600493e+08 23.322 Stage4 20.759 1.036157e+09 0.914 1.728123e+08 6.212647e+09 22.716 Purity 1.747 5.739000e+00 4.243 1.000000e-03 2.348883e+04 0.412 p signif ACAT1 0.358 Age 0.034 * Gendermale 0.316 RaceBlack 0.999 RaceWhite 0.183 Stage2 0.000 *** Stage3 0.000 *** Stage4 0.000 *** Purity 0.681 Rsquare = 0.352 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.25e-03 Wald test p = 1.1e-308 Score (logrank) test p = 5.86e-09 ACAT1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.389 0.678 0.083 0.576 0.798 -4.681 0.000 *** Age 0.032 1.032 0.008 1.016 1.049 3.878 0.000 *** Gendermale -0.276 0.759 0.188 0.525 1.097 -1.467 0.142 RaceBlack 0.889 2.432 1.065 0.301 19.625 0.834 0.404 RaceWhite 0.902 2.465 1.029 0.328 18.542 0.876 0.381 Stage2 0.231 1.260 0.346 0.639 2.484 0.667 0.505 Stage3 0.652 1.920 0.233 1.215 3.032 2.797 0.005 ** Stage4 1.589 4.899 0.220 3.185 7.536 7.233 0.000 *** Purity 0.218 1.244 0.358 0.617 2.509 0.610 0.542 Rsquare = 0.213 (max possible = 9.65e-01 ) Likelihood ratio test p = 3.71e-19 Wald test p = 6.4e-20 Score (logrank) test p = 6.66e-23 ACAT1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.423 0.655 0.157 0.481 0.892 -2.688 0.007 ** Age 0.011 1.011 0.016 0.980 1.043 0.676 0.499 Gendermale -0.336 0.714 0.390 0.333 1.534 -0.863 0.388 RaceBlack -1.976 0.139 1.211 0.013 1.488 -1.632 0.103 RaceWhite -2.101 0.122 1.176 0.012 1.226 -1.787 0.074 · Stage2 -0.334 0.716 1.058 0.090 5.700 -0.315 0.753 Stage3 1.430 4.181 0.433 1.788 9.775 3.301 0.001 ** Stage4 2.407 11.103 0.516 4.041 30.503 4.669 0.000 *** Purity -0.104 0.901 0.778 0.196 4.138 -0.134 0.893 Rsquare = 0.188 (max possible = 7.58e-01 ) Likelihood ratio test p = 7.38e-07 Wald test p = 2.84e-07 Score (logrank) test p = 1.53e-11 ACAT1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.108 1.114 0.157 0.818 1.517 0.686 0.493 Age 0.038 1.039 0.008 1.022 1.056 4.687 0.000 *** Gendermale -0.151 0.860 0.213 0.566 1.305 -0.709 0.478 RaceBlack -0.291 0.747 1.109 0.085 6.564 -0.263 0.793 RaceWhite -0.684 0.505 1.018 0.069 3.713 -0.671 0.502 Rsquare = 0.158 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.03e-04 Wald test p = 3.36e-04 Score (logrank) test p = 2.42e-04 ACAT1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.832 0.435 0.270 0.257 0.738 -3.085 0.002 ** Age 0.063 1.065 0.008 1.049 1.081 8.233 0.000 *** Gendermale 0.045 1.046 0.195 0.713 1.534 0.230 0.818 RaceBlack 16.552 15429395.128 2895.871 0.000 Inf 0.006 0.995 RaceWhite 16.573 15755444.260 2895.871 0.000 Inf 0.006 0.995 Purity -0.605 0.546 0.423 0.239 1.251 -1.431 0.153 Rsquare = 0.153 (max possible = 9.07e-01 ) Likelihood ratio test p = 1.47e-14 Wald test p = 4.95e-15 Score (logrank) test p = 1.26e-15 ACAT1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.250 0.778 0.093 0.648 0.935 -2.680 0.007 ** Age 0.008 1.008 0.008 0.992 1.024 0.983 0.326 Gendermale -0.115 0.892 0.224 0.575 1.382 -0.513 0.608 RaceBlack 0.973 2.646 0.493 1.006 6.958 1.973 0.048 * RaceWhite 0.118 1.126 0.240 0.703 1.803 0.492 0.622 Stage2 0.194 1.214 0.267 0.720 2.046 0.727 0.467 Stage3 0.819 2.269 0.241 1.413 3.642 3.393 0.001 ** Stage4 1.567 4.792 0.617 1.429 16.068 2.538 0.011 * Purity 0.598 1.819 0.460 0.738 4.483 1.299 0.194 Rsquare = 0.104 (max possible = 9.66e-01 ) Likelihood ratio test p = 8.62e-05 Wald test p = 3.14e-05 Score (logrank) test p = 1.14e-05 ACAT1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.331 0.718 0.121 0.566 0.911 -2.731 0.006 ** Age 0.010 1.010 0.009 0.992 1.028 1.085 0.278 Gendermale 0.117 1.124 0.173 0.801 1.577 0.678 0.498 RaceBlack 15.949 8446510.326 1955.393 0.000 Inf 0.008 0.993 RaceWhite 16.147 10294931.858 1955.393 0.000 Inf 0.008 0.993 Stage2 0.840 2.317 0.201 1.563 3.435 4.181 0.000 *** Stage3 0.965 2.624 0.218 1.710 4.025 4.418 0.000 *** Stage4 1.011 2.749 0.333 1.430 5.286 3.033 0.002 ** Purity 0.749 2.114 0.346 1.073 4.168 2.163 0.031 * Rsquare = 0.113 (max possible = 9.74e-01 ) Likelihood ratio test p = 9.12e-08 Wald test p = 1.46e-06 Score (logrank) test p = 1.32e-07 ACAT1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.108 1.114 0.130 0.862 1.438 0.824 0.410 Age 0.017 1.017 0.009 0.998 1.036 1.795 0.073 · Gendermale 0.431 1.538 0.193 1.054 2.246 2.230 0.026 * RaceBlack -0.017 0.983 0.605 0.300 3.220 -0.028 0.977 RaceWhite -0.544 0.581 0.562 0.193 1.746 -0.968 0.333 Stage2 0.215 1.240 0.187 0.860 1.788 1.151 0.250 Stage3 0.626 1.871 0.216 1.225 2.858 2.898 0.004 ** Stage4 0.777 2.176 0.792 0.461 10.273 0.982 0.326 Purity -0.355 0.701 0.365 0.343 1.433 -0.974 0.330 Rsquare = 0.052 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.92e-02 Wald test p = 1.44e-02 Score (logrank) test p = 1.23e-02 ACAT1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.032 1.032 0.258 0.622 1.711 0.122 0.903 Age 0.020 1.020 0.016 0.989 1.052 1.274 0.203 Gendermale -0.183 0.833 0.331 0.435 1.594 -0.552 0.581 RaceBlack 0.111 1.117 1.542 0.054 22.955 0.072 0.943 RaceWhite -0.508 0.601 1.046 0.077 4.669 -0.486 0.627 Stage2 -0.237 0.789 0.469 0.315 1.978 -0.506 0.613 Stage3 -0.110 0.896 0.422 0.392 2.049 -0.260 0.795 Stage4 -0.153 0.858 0.477 0.337 2.186 -0.320 0.749 Purity -0.753 0.471 0.554 0.159 1.394 -1.360 0.174 Rsquare = 0.06 (max possible = 9.98e-01 ) Likelihood ratio test p = 8.1e-01 Wald test p = 7.89e-01 Score (logrank) test p = 7.82e-01 ACAT1 in OV (n=303): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.063 1.065 0.120 0.842 1.346 0.526 0.599 Age 0.037 1.038 0.008 1.021 1.054 4.459 0.000 *** RaceBlack -0.028 0.972 0.578 0.313 3.020 -0.049 0.961 RaceWhite -0.142 0.868 0.516 0.316 2.385 -0.275 0.783 Purity -0.590 0.554 0.672 0.148 2.070 -0.878 0.380 Rsquare = 0.082 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.03e-03 Wald test p = 8.97e-04 Score (logrank) test p = 7.43e-04 ACAT1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.446 0.640 0.209 0.425 0.965 -2.128 0.033 * Age 0.021 1.021 0.011 1.000 1.044 1.933 0.053 · Gendermale -0.302 0.739 0.219 0.481 1.136 -1.379 0.168 RaceBlack 0.406 1.501 0.767 0.334 6.755 0.529 0.597 RaceWhite 0.515 1.674 0.483 0.649 4.318 1.066 0.286 Stage2 0.289 1.336 0.459 0.543 3.286 0.630 0.528 Stage3 -0.479 0.620 1.094 0.073 5.292 -0.438 0.662 Stage4 -0.077 0.925 0.834 0.181 4.743 -0.093 0.926 Purity -0.900 0.406 0.440 0.172 0.963 -2.047 0.041 * Rsquare = 0.113 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.92e-02 Wald test p = 4.13e-02 Score (logrank) test p = 3.62e-02 ACAT1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.304 1.356 0.657 0.374 4.913 0.463 0.643 Age 0.039 1.040 0.028 0.984 1.099 1.391 0.164 Gendermale 1.357 3.886 0.896 0.671 22.512 1.514 0.130 RaceBlack -0.449 0.638 19577.554 0.000 Inf 0.000 1.000 RaceWhite 17.161 28365484.783 15909.897 0.000 Inf 0.001 0.999 Purity 5.831 340.538 3.481 0.371 312577.225 1.675 0.094 · Rsquare = 0.056 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.5e-01 Wald test p = 3.78e-01 Score (logrank) test p = 3.09e-01 ACAT1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.762 2.142 0.585 0.680 6.744 1.302 0.193 Age 0.005 1.005 0.055 0.902 1.119 0.085 0.932 RaceBlack 14.964 3154667.297 6896.569 0.000 Inf 0.002 0.998 RaceWhite 16.456 14021779.144 6896.569 0.000 Inf 0.002 0.998 Purity 0.976 2.654 1.366 0.182 38.627 0.714 0.475 Rsquare = 0.011 (max possible = 1.83e-01 ) Likelihood ratio test p = 4.9e-01 Wald test p = 6.15e-01 Score (logrank) test p = 5.71e-01 ACAT1 in READ (n=166): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.134 0.874 0.497 0.330 2.318 -0.270 0.787 Age 0.107 1.113 0.044 1.021 1.214 2.424 0.015 * Gendermale -0.389 0.677 0.703 0.171 2.688 -0.554 0.580 RaceBlack 13.603 808572.720 10269.534 0.000 Inf 0.001 0.999 RaceWhite 12.514 272051.427 10269.534 0.000 Inf 0.001 0.999 Stage2 -1.881 0.153 1.256 0.013 1.788 -1.497 0.134 Stage3 -0.545 0.580 0.935 0.093 3.627 -0.583 0.560 Stage4 -0.213 0.808 0.984 0.118 5.556 -0.216 0.829 Purity -0.097 0.907 1.584 0.041 20.236 -0.061 0.951 Rsquare = 0.21 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.62e-02 Wald test p = 2.2e-01 Score (logrank) test p = 4.36e-02 ACAT1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.172 0.842 0.158 0.618 1.147 -1.091 0.275 Age 0.023 1.023 0.008 1.007 1.040 2.753 0.006 ** Gendermale -0.033 0.967 0.223 0.625 1.497 -0.150 0.881 RaceBlack -0.136 0.873 1.087 0.104 7.347 -0.125 0.900 RaceWhite -0.494 0.610 1.022 0.082 4.520 -0.484 0.629 Purity 1.104 3.015 0.594 0.942 9.655 1.859 0.063 · Rsquare = 0.047 (max possible = 9.75e-01 ) Likelihood ratio test p = 7.91e-02 Wald test p = 9.99e-02 Score (logrank) test p = 1.03e-01 ACAT1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.144 0.866 0.107 0.703 1.067 -1.349 0.177 Age 0.018 1.018 0.005 1.008 1.028 3.437 0.001 ** Gendermale -0.057 0.944 0.157 0.694 1.285 -0.365 0.715 RaceWhite -1.268 0.281 0.402 0.128 0.619 -3.155 0.002 ** Stage2 0.304 1.355 0.219 0.882 2.082 1.389 0.165 Stage3 0.619 1.858 0.204 1.245 2.773 3.030 0.002 ** Stage4 1.355 3.878 0.352 1.946 7.731 3.851 0.000 *** Purity 1.068 2.908 0.342 1.486 5.691 3.117 0.002 ** Rsquare = 0.127 (max possible = 9.92e-01 ) Likelihood ratio test p = 9.97e-09 Wald test p = 5.02e-09 Score (logrank) test p = 5.62e-10 ACAT1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.073 9.290000e-01 0.254 0.565 1.528 -0.289 0.772 Age 0.012 1.012000e+00 0.016 0.980 1.044 0.722 0.470 Gendermale 0.219 1.244000e+00 0.433 0.533 2.904 0.505 0.613 RaceWhite -1.290 2.750000e-01 0.629 0.080 0.945 -2.050 0.040 * Stage2 17.499 3.976968e+07 6195.460 0.000 Inf 0.003 0.998 Stage3 17.961 6.314231e+07 6195.460 0.000 Inf 0.003 0.998 Stage4 20.164 5.714675e+08 6195.460 0.000 Inf 0.003 0.997 Purity 0.357 1.429000e+00 0.993 0.204 9.995 0.359 0.719 Rsquare = 0.147 (max possible = 8.69e-01 ) Likelihood ratio test p = 5.97e-02 Wald test p = 5.22e-02 Score (logrank) test p = 4.35e-03 ACAT1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -0.152 0.859 0.118 0.682 1.081 -1.296 0.195 Age 0.020 1.020 0.006 1.009 1.031 3.552 0.000 *** Gendermale -0.065 0.937 0.172 0.669 1.312 -0.378 0.705 RaceWhite -1.003 0.367 0.601 0.113 1.191 -1.669 0.095 · Stage2 0.183 1.201 0.231 0.763 1.889 0.792 0.429 Stage3 0.576 1.779 0.209 1.180 2.681 2.753 0.006 ** Stage4 1.132 3.102 0.400 1.416 6.795 2.830 0.005 ** Purity 1.182 3.261 0.372 1.574 6.755 3.182 0.001 ** Rsquare = 0.139 (max possible = 9.95e-01 ) Likelihood ratio test p = 5.32e-07 Wald test p = 7.25e-07 Score (logrank) test p = 2.64e-07 ACAT1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.144 1.155 0.150 0.861 1.551 0.961 0.337 Age 0.028 1.028 0.010 1.008 1.049 2.711 0.007 ** Gendermale 0.134 1.143 0.208 0.760 1.719 0.644 0.520 RaceBlack 0.189 1.208 0.456 0.495 2.951 0.415 0.678 RaceWhite 0.095 1.099 0.244 0.681 1.774 0.387 0.698 Stage2 0.516 1.675 0.392 0.777 3.611 1.315 0.188 Stage3 0.956 2.602 0.367 1.267 5.340 2.606 0.009 ** Stage4 1.386 4.001 0.510 1.472 10.876 2.717 0.007 ** Purity -0.535 0.586 0.381 0.278 1.236 -1.404 0.160 Rsquare = 0.072 (max possible = 9.79e-01 ) Likelihood ratio test p = 9.89e-03 Wald test p = 1.34e-02 Score (logrank) test p = 1.08e-02 ACAT1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 -10.019 0.000000e+00 50046.428 0 Inf 0.000 1.000 Age -1.864 1.550000e-01 1696.002 0 Inf -0.001 0.999 RaceBlack 12.117 1.829064e+05 18569634.411 0 Inf 0.000 1.000 RaceWhite -38.378 0.000000e+00 17375729.116 0 Inf 0.000 1.000 Stage2 -7.645 0.000000e+00 40600.427 0 Inf 0.000 1.000 Stage3 19.542 3.069358e+08 118936.094 0 Inf 0.000 1.000 Purity 48.786 1.540208e+21 193993.160 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.3e-03 ACAT1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.345 1.412 0.415 0.626 3.187 0.831 0.406 Age 0.148 1.160 0.028 1.097 1.226 5.252 0.000 *** Gendermale 0.129 1.137 0.651 0.318 4.071 0.198 0.843 RaceBlack 17.167 28538573.384 5934.386 0.000 Inf 0.003 0.998 RaceWhite 17.110 26957408.112 5934.386 0.000 Inf 0.003 0.998 Stage2 0.036 1.037 1.105 0.119 9.039 0.033 0.974 Stage3 0.367 1.443 0.859 0.268 7.770 0.427 0.669 Stage4 1.829 6.226 0.972 0.927 41.812 1.882 0.060 · Purity 2.138 8.481 1.069 1.044 68.895 2.000 0.045 * Rsquare = 0.151 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.85e-10 Wald test p = 3.1e-04 Score (logrank) test p = 1.19e-10 ACAT1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.736 2.088 0.623 0.615 7.086 1.181 0.237 Age 0.055 1.056 0.033 0.991 1.126 1.685 0.092 · Gendermale 0.153 1.165 0.799 0.243 5.583 0.192 0.848 RaceBlack -16.976 0.000 10440.517 0.000 Inf -0.002 0.999 RaceWhite 0.061 1.063 1.146 0.113 10.038 0.053 0.958 Purity 0.282 1.325 1.134 0.144 12.230 0.248 0.804 Rsquare = 0.057 (max possible = 4.51e-01 ) Likelihood ratio test p = 3.62e-01 Wald test p = 5.13e-01 Score (logrank) test p = 3.88e-01 ACAT1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.100 1.106 0.178 0.780 1.567 0.565 0.572 Age 0.050 1.052 0.016 1.019 1.085 3.179 0.001 ** RaceBlack -0.448 0.639 0.796 0.134 3.044 -0.562 0.574 RaceWhite -0.528 0.589 0.745 0.137 2.540 -0.709 0.478 Purity 0.479 1.614 0.649 0.452 5.764 0.737 0.461 Rsquare = 0.039 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.44e-02 Wald test p = 5.18e-02 Score (logrank) test p = 5.14e-02 ACAT1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.243 1.275 0.244 0.790 2.059 0.995 0.320 Age 0.047 1.048 0.024 0.999 1.100 1.939 0.052 · RaceBlack 17.518 40562889.670 6525.744 0.000 Inf 0.003 0.998 RaceWhite 17.793 53362971.119 6525.744 0.000 Inf 0.003 0.998 Purity -0.407 0.665 1.160 0.069 6.461 -0.351 0.725 Rsquare = 0.135 (max possible = 9.83e-01 ) Likelihood ratio test p = 1.82e-01 Wald test p = 2.72e-01 Score (logrank) test p = 1.95e-01 ACAT1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `ACAT1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT1 0.017 1.017 0.314 0.550 1.881 0.053 0.958 Age 0.040 1.041 0.019 1.003 1.081 2.105 0.035 * Gendermale 0.272 1.313 0.483 0.509 3.385 0.563 0.574 Stage3 0.281 1.324 0.503 0.494 3.549 0.558 0.577 Stage4 3.756 42.793 1.218 3.935 465.414 3.085 0.002 ** Purity 1.954 7.059 1.233 0.629 79.194 1.584 0.113 Rsquare = 0.253 (max possible = 8.72e-01 ) Likelihood ratio test p = 1.01e-03 Wald test p = 3.55e-03 Score (logrank) test p = 2.63e-09 ACAT2 in ACC (n=79): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.255 1.290 0.131 0.998 1.668 1.947 0.052 · Age 0.014 1.014 0.015 0.985 1.045 0.958 0.338 Gendermale 0.658 1.932 0.448 0.802 4.650 1.469 0.142 RaceBlack -0.889 0.411 12465.013 0.000 Inf 0.000 1.000 RaceWhite 16.047 9309244.063 10654.698 0.000 Inf 0.002 0.999 Purity 2.189 8.923 2.354 0.089 899.686 0.930 0.352 Rsquare = 0.126 (max possible = 9.38e-01 ) Likelihood ratio test p = 1.96e-01 Wald test p = 4.34e-01 Score (logrank) test p = 3.01e-01 ACAT2 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.140 1.150 0.103 0.940 1.406 1.362 0.173 Age 0.033 1.033 0.009 1.016 1.051 3.805 0.000 *** Gendermale -0.173 0.841 0.179 0.593 1.194 -0.968 0.333 RaceBlack 0.650 1.916 0.449 0.794 4.620 1.448 0.148 RaceWhite 0.084 1.087 0.355 0.542 2.182 0.236 0.814 Stage2 14.579 2146173.625 1862.263 0.000 Inf 0.008 0.994 Stage3 15.052 3445041.073 1862.263 0.000 Inf 0.008 0.994 Stage4 15.556 5698775.145 1862.263 0.000 Inf 0.008 0.993 Purity 0.031 1.032 0.349 0.521 2.043 0.090 0.929 Rsquare = 0.135 (max possible = 9.91e-01 ) Likelihood ratio test p = 8.98e-08 Wald test p = 5.57e-07 Score (logrank) test p = 1.59e-07 ACAT2 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.077 1.080 0.122 0.850 1.370 0.629 0.529 Age 0.036 1.037 0.008 1.022 1.053 4.772 0.000 *** Gendermale 0.035 1.036 1.007 0.144 7.456 0.035 0.972 RaceBlack -0.019 0.981 0.620 0.291 3.305 -0.031 0.975 RaceWhite -0.239 0.787 0.597 0.245 2.535 -0.401 0.689 Stage2 0.409 1.506 0.304 0.830 2.732 1.347 0.178 Stage3 1.197 3.312 0.313 1.792 6.120 3.821 0.000 *** Stage4 2.502 12.207 0.389 5.695 26.164 6.432 0.000 *** Purity 0.484 1.622 0.424 0.706 3.724 1.140 0.254 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.08e-12 Wald test p = 6.22e-16 Score (logrank) test p = 7.29e-22 ACAT2 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.274 7.610000e-01 0.202 0.512 1.129 -1.357 0.175 Age 0.008 1.008000e+00 0.017 0.975 1.043 0.486 0.627 RaceBlack -1.127 3.240000e-01 1.124 0.036 2.937 -1.002 0.316 RaceWhite -1.477 2.280000e-01 1.145 0.024 2.154 -1.290 0.197 Stage2 18.630 1.232499e+08 6492.439 0.000 Inf 0.003 0.998 Stage3 20.036 5.029466e+08 6492.439 0.000 Inf 0.003 0.998 Stage4 21.206 1.620817e+09 6492.439 0.000 Inf 0.003 0.997 Purity 0.818 2.266000e+00 0.946 0.355 14.467 0.865 0.387 Rsquare = 0.165 (max possible = 7.18e-01 ) Likelihood ratio test p = 2.6e-04 Wald test p = 4.08e-03 Score (logrank) test p = 2.09e-06 ACAT2 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.174 8.410000e-01 0.478 0.329 2.147 -0.363 0.717 Age 0.036 1.037000e+00 0.030 0.977 1.100 1.189 0.234 RaceBlack -2.965 5.200000e-02 1.802 0.002 1.765 -1.645 0.100 RaceWhite -1.701 1.830000e-01 1.447 0.011 3.109 -1.176 0.240 Stage2 18.128 7.460217e+07 14771.352 0.000 Inf 0.001 0.999 Stage3 19.834 4.109550e+08 14771.352 0.000 Inf 0.001 0.999 Stage4 52.726 7.917459e+22 1913113.577 0.000 Inf 0.000 1.000 Purity 3.094 2.206100e+01 2.255 0.265 1834.420 1.372 0.170 Rsquare = 0.371 (max possible = 6.68e-01 ) Likelihood ratio test p = 6.06e-04 Wald test p = 1e+00 Score (logrank) test p = 3.72e-14 ACAT2 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.148 1.159 0.245 0.718 1.873 0.604 0.546 Age 0.050 1.051 0.012 1.027 1.077 4.110 0.000 *** Gendermale -15.437 0.000 3466.789 0.000 Inf -0.004 0.996 RaceBlack -0.454 0.635 1.174 0.064 6.342 -0.387 0.699 RaceWhite 0.195 1.215 1.035 0.160 9.246 0.188 0.851 Stage2 0.364 1.439 0.380 0.683 3.032 0.957 0.339 Stage3 0.925 2.522 0.408 1.132 5.616 2.264 0.024 * Stage4 2.167 8.730 0.592 2.734 27.876 3.658 0.000 *** Purity 0.299 1.348 0.616 0.403 4.512 0.485 0.628 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 8.99e-05 Wald test p = 1.86e-05 Score (logrank) test p = 3.62e-07 ACAT2 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.005 0.995 0.271 0.585 1.694 -0.018 0.986 Age 0.050 1.051 0.021 1.009 1.095 2.400 0.016 * Gendermale 0.977 2.655 1.106 0.304 23.193 0.883 0.377 RaceBlack 16.569 15705073.878 6464.612 0.000 Inf 0.003 0.998 RaceWhite 15.948 8432439.667 6464.612 0.000 Inf 0.002 0.998 Stage2 0.683 1.980 1.075 0.241 16.282 0.635 0.525 Stage3 1.601 4.957 1.061 0.619 39.665 1.509 0.131 Stage4 2.090 8.088 1.172 0.813 80.486 1.783 0.075 · Purity 1.041 2.831 1.317 0.214 37.407 0.790 0.429 Rsquare = 0.105 (max possible = 6.98e-01 ) Likelihood ratio test p = 4.2e-02 Wald test p = 7.14e-02 Score (logrank) test p = 2.46e-02 ACAT2 in CESC (n=306): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.031 1.032 0.149 0.770 1.383 0.210 0.834 Age 0.011 1.011 0.010 0.991 1.031 1.070 0.285 RaceBlack 1.065 2.900 1.071 0.355 23.662 0.994 0.320 RaceWhite 0.836 2.308 1.017 0.314 16.950 0.822 0.411 Purity 0.562 1.755 0.734 0.416 7.398 0.766 0.444 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.73e-01 Wald test p = 7.07e-01 Score (logrank) test p = 6.99e-01 ACAT2 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.850 2.339 0.403 1.062 5.153 2.109 0.035 * Age 0.015 1.015 0.022 0.972 1.060 0.687 0.492 Gendermale 0.247 1.281 0.575 0.415 3.950 0.431 0.667 RaceBlack -1.259 0.284 1.569 0.013 6.145 -0.802 0.422 RaceWhite -0.957 0.384 0.871 0.070 2.119 -1.098 0.272 Stage2 0.476 1.609 0.658 0.443 5.846 0.723 0.470 Stage3 -14.171 0.000 7300.147 0.000 Inf -0.002 0.998 Stage4 0.595 1.812 0.710 0.451 7.284 0.838 0.402 Purity 4.365 78.646 1.945 1.739 3557.256 2.244 0.025 * Rsquare = 0.306 (max possible = 9.46e-01 ) Likelihood ratio test p = 1.55e-01 Wald test p = 2.78e-01 Score (logrank) test p = 1.55e-01 ACAT2 in COAD (n=458): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.251 0.778 0.208 0.518 1.168 -1.210 0.226 Age 0.024 1.024 0.012 1.001 1.048 2.061 0.039 * Gendermale 0.218 1.243 0.271 0.731 2.115 0.804 0.421 RaceBlack -0.562 0.570 0.832 0.112 2.912 -0.675 0.500 RaceWhite -0.586 0.556 0.783 0.120 2.580 -0.749 0.454 Stage2 0.219 1.245 0.562 0.413 3.749 0.389 0.697 Stage3 0.819 2.268 0.549 0.773 6.649 1.492 0.136 Stage4 1.914 6.779 0.552 2.297 20.012 3.465 0.001 ** Purity -0.166 0.847 0.588 0.267 2.683 -0.283 0.777 Rsquare = 0.114 (max possible = 9.04e-01 ) Likelihood ratio test p = 2.62e-04 Wald test p = 8.53e-05 Score (logrank) test p = 1.26e-05 ACAT2 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.840 0.432 1.210 0.040 4.626 -0.694 0.488 Age 0.006 1.006 0.041 0.928 1.091 0.150 0.881 Gendermale 0.565 1.760 1.070 0.216 14.328 0.528 0.597 RaceBlack -0.544 0.580 2.048 0.010 32.150 -0.266 0.791 RaceWhite -2.036 0.130 1.236 0.012 1.470 -1.648 0.099 · Purity -2.540 0.079 2.380 0.001 8.372 -1.067 0.286 Rsquare = 0.142 (max possible = 5.58e-01 ) Likelihood ratio test p = 3.91e-01 Wald test p = 5.6e-01 Score (logrank) test p = 2.85e-01 ACAT2 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.156 1.169 0.205 0.782 1.747 0.762 0.446 Age 0.011 1.011 0.014 0.984 1.039 0.777 0.437 Gendermale 0.535 1.707 0.542 0.590 4.936 0.986 0.324 RaceBlack 0.371 1.449 1.069 0.178 11.769 0.347 0.728 RaceWhite -0.030 0.971 0.453 0.399 2.361 -0.066 0.948 Stage2 0.743 2.102 0.662 0.574 7.694 1.122 0.262 Stage3 1.516 4.555 0.681 1.200 17.293 2.227 0.026 * Stage4 2.843 17.166 0.780 3.723 79.158 3.645 0.000 *** Purity 0.019 1.019 0.805 0.210 4.939 0.024 0.981 Rsquare = 0.145 (max possible = 9.32e-01 ) Likelihood ratio test p = 9.29e-03 Wald test p = 4.95e-03 Score (logrank) test p = 4.03e-04 ACAT2 in GBM (n=153): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.003 0.997 0.185 0.694 1.433 -0.014 0.989 Age 0.030 1.030 0.008 1.013 1.047 3.566 0.000 *** Gendermale -0.095 0.909 0.213 0.599 1.380 -0.447 0.655 RaceBlack 0.528 1.696 0.728 0.407 7.067 0.726 0.468 RaceWhite -0.240 0.786 0.614 0.236 2.621 -0.391 0.696 Purity -1.082 0.339 0.607 0.103 1.114 -1.782 0.075 · Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.8e-03 Wald test p = 6.87e-03 Score (logrank) test p = 5.93e-03 ACAT2 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.020 1.021 0.073 0.885 1.177 0.282 0.778 Age 0.022 1.022 0.008 1.007 1.038 2.888 0.004 ** Gendermale -0.256 0.774 0.174 0.551 1.088 -1.476 0.140 RaceBlack 0.099 1.104 0.572 0.360 3.388 0.173 0.863 RaceWhite -0.275 0.759 0.520 0.274 2.106 -0.529 0.597 Stage2 0.625 1.869 0.544 0.643 5.432 1.148 0.251 Stage3 0.859 2.362 0.538 0.824 6.773 1.599 0.110 Stage4 1.257 3.516 0.510 1.294 9.554 2.465 0.014 * Purity -0.063 0.939 0.368 0.456 1.933 -0.171 0.865 Rsquare = 0.07 (max possible = 9.89e-01 ) Likelihood ratio test p = 5.11e-04 Wald test p = 1.33e-03 Score (logrank) test p = 9.77e-04 ACAT2 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.156 8.550000e-01 0.209 0.568 1.287 -0.750 0.453 Age 0.012 1.012000e+00 0.025 0.963 1.064 0.482 0.630 Gendermale -0.184 8.320000e-01 0.545 0.286 2.421 -0.338 0.735 RaceBlack 18.965 1.723975e+08 12176.652 0.000 Inf 0.002 0.999 RaceWhite 18.201 8.028274e+07 12176.652 0.000 Inf 0.001 0.999 Stage2 17.387 3.556866e+07 5327.446 0.000 Inf 0.003 0.997 Stage3 16.632 1.671193e+07 5327.446 0.000 Inf 0.003 0.998 Stage4 17.397 3.591603e+07 5327.446 0.000 Inf 0.003 0.997 Purity -1.251 2.860000e-01 1.140 0.031 2.673 -1.097 0.272 Rsquare = 0.095 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.93e-01 Wald test p = 9.11e-01 Score (logrank) test p = 7.98e-01 ACAT2 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.067 1.069 0.080 0.914 1.250 0.838 0.402 Age 0.026 1.027 0.008 1.010 1.044 3.141 0.002 ** Gendermale -0.313 0.731 0.185 0.508 1.052 -1.687 0.092 · RaceBlack -0.140 0.869 0.581 0.278 2.717 -0.241 0.810 RaceWhite -0.494 0.610 0.525 0.218 1.706 -0.942 0.346 Stage2 0.393 1.481 0.554 0.500 4.389 0.709 0.479 Stage3 0.755 2.128 0.542 0.736 6.159 1.393 0.164 Stage4 1.154 3.171 0.512 1.162 8.655 2.253 0.024 * Purity 0.177 1.193 0.402 0.543 2.624 0.440 0.660 Rsquare = 0.087 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.56e-04 Wald test p = 7.05e-04 Score (logrank) test p = 5.37e-04 ACAT2 in KICH (n=66): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p ACAT2 1.586 4.882 0.525 1.745 1.365900e+01 3.021 0.003 Age 0.127 1.135 0.030 1.070 1.205000e+00 4.193 0.000 Gendermale -2.084 0.124 0.730 0.030 5.200000e-01 -2.857 0.004 RaceBlack -14.533 0.000 2664.271 0.000 Inf -0.005 0.996 RaceWhite -1.947 0.143 1.156 0.015 1.376000e+00 -1.684 0.092 Stage2 14.645 2291735.834 0.846 436655.186 1.202792e+07 17.313 0.000 Stage3 16.295 11929247.466 0.789 2543030.478 5.595959e+07 20.662 0.000 Stage4 17.080 26178013.497 0.896 4522747.869 1.515204e+08 19.067 0.000 Purity 0.680 1.974 3.594 0.002 2.264474e+03 0.189 0.850 signif ACAT2 ** Age *** Gendermale ** RaceBlack RaceWhite · Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.378 (max possible = 6.71e-01 ) Likelihood ratio test p = 4.49e-04 Wald test p = 4.33e-237 Score (logrank) test p = 7.33e-09 ACAT2 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.191 0.826 0.176 0.586 1.166 -1.087 0.277 Age 0.034 1.035 0.008 1.018 1.052 4.101 0.000 *** Gendermale -0.105 0.900 0.186 0.626 1.295 -0.566 0.571 RaceBlack 0.296 1.344 1.059 0.168 10.721 0.279 0.780 RaceWhite 0.229 1.258 1.017 0.171 9.235 0.225 0.822 Stage2 0.182 1.200 0.346 0.609 2.364 0.526 0.599 Stage3 0.812 2.253 0.230 1.436 3.533 3.536 0.000 *** Stage4 1.786 5.965 0.218 3.893 9.141 8.202 0.000 *** Purity -0.087 0.916 0.376 0.439 1.914 -0.232 0.816 Rsquare = 0.176 (max possible = 9.65e-01 ) Likelihood ratio test p = 6.13e-15 Wald test p = 8.23e-15 Score (logrank) test p = 3.93e-18 ACAT2 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.208 1.231 0.292 0.694 2.185 0.712 0.477 Age 0.008 1.008 0.016 0.977 1.039 0.496 0.620 Gendermale -0.480 0.619 0.381 0.294 1.305 -1.260 0.208 RaceBlack -2.211 0.110 1.220 0.010 1.196 -1.813 0.070 · RaceWhite -2.266 0.104 1.206 0.010 1.102 -1.879 0.060 · Stage2 -0.419 0.657 1.055 0.083 5.202 -0.397 0.691 Stage3 1.652 5.218 0.427 2.260 12.048 3.869 0.000 *** Stage4 2.723 15.230 0.509 5.611 41.338 5.346 0.000 *** Purity -0.334 0.716 0.753 0.164 3.134 -0.443 0.658 Rsquare = 0.165 (max possible = 7.58e-01 ) Likelihood ratio test p = 9.42e-06 Wald test p = 4.59e-06 Score (logrank) test p = 6.54e-10 ACAT2 in LAML (n=173): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.584 1.793 0.220 1.166 2.758 2.657 0.008 ** Age 0.040 1.041 0.008 1.024 1.057 4.976 0.000 *** Gendermale -0.284 0.752 0.218 0.490 1.154 -1.302 0.193 RaceBlack -0.185 0.831 1.104 0.095 7.236 -0.168 0.867 RaceWhite -0.713 0.490 1.018 0.067 3.605 -0.700 0.484 Rsquare = 0.199 (max possible = 9.96e-01 ) Likelihood ratio test p = 3.72e-06 Wald test p = 1.09e-05 Score (logrank) test p = 8.57e-06 ACAT2 in LGG (n=516): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.283 0.753 0.115 0.601 0.944 -2.456 0.014 * Age 0.063 1.065 0.008 1.049 1.081 8.264 0.000 *** Gendermale 0.029 1.029 0.196 0.701 1.512 0.147 0.883 RaceBlack 15.379 4774967.964 2086.250 0.000 Inf 0.007 0.994 RaceWhite 15.456 5159332.025 2086.250 0.000 Inf 0.007 0.994 Purity -0.744 0.475 0.415 0.211 1.072 -1.793 0.073 · Rsquare = 0.148 (max possible = 9.07e-01 ) Likelihood ratio test p = 5.82e-14 Wald test p = 2.73e-14 Score (logrank) test p = 2.82e-15 ACAT2 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.073 1.076 0.108 0.870 1.331 0.674 0.500 Age 0.011 1.011 0.008 0.995 1.027 1.349 0.177 Gendermale -0.132 0.876 0.226 0.562 1.366 -0.583 0.560 RaceBlack 0.831 2.296 0.498 0.866 6.088 1.670 0.095 · RaceWhite 0.004 1.004 0.237 0.631 1.598 0.017 0.987 Stage2 0.342 1.407 0.264 0.839 2.361 1.295 0.195 Stage3 0.947 2.579 0.235 1.628 4.085 4.036 0.000 *** Stage4 1.625 5.078 0.620 1.505 17.126 2.619 0.009 ** Purity 0.560 1.750 0.458 0.714 4.290 1.223 0.221 Rsquare = 0.086 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.01e-03 Wald test p = 5.55e-04 Score (logrank) test p = 2.13e-04 ACAT2 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.287 1.333 0.115 1.063 1.671 2.488 0.013 * Age 0.008 1.008 0.009 0.991 1.026 0.915 0.360 Gendermale 0.036 1.037 0.169 0.745 1.443 0.213 0.831 RaceBlack 16.208 10943808.780 1826.691 0.000 Inf 0.009 0.993 RaceWhite 16.435 13725004.715 1826.691 0.000 Inf 0.009 0.993 Stage2 0.838 2.311 0.201 1.557 3.430 4.159 0.000 *** Stage3 0.962 2.616 0.219 1.703 4.019 4.390 0.000 *** Stage4 0.946 2.576 0.334 1.338 4.958 2.832 0.005 ** Purity 0.623 1.864 0.345 0.947 3.670 1.803 0.071 · Rsquare = 0.109 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.87e-07 Wald test p = 3.1e-06 Score (logrank) test p = 4.15e-07 ACAT2 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.042 0.959 0.103 0.783 1.174 -0.408 0.683 Age 0.016 1.017 0.009 0.998 1.035 1.767 0.077 · Gendermale 0.443 1.558 0.194 1.064 2.280 2.279 0.023 * RaceBlack 0.026 1.026 0.606 0.313 3.364 0.042 0.966 RaceWhite -0.498 0.608 0.563 0.202 1.833 -0.884 0.377 Stage2 0.214 1.238 0.187 0.859 1.786 1.145 0.252 Stage3 0.609 1.839 0.215 1.207 2.801 2.836 0.005 ** Stage4 0.756 2.130 0.791 0.452 10.043 0.956 0.339 Purity -0.329 0.720 0.367 0.350 1.478 -0.896 0.370 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.29e-02 Wald test p = 1.79e-02 Score (logrank) test p = 1.54e-02 ACAT2 in MESO (n=87): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.545 1.725 0.204 1.157 2.573 2.674 0.008 ** Age 0.005 1.005 0.017 0.971 1.039 0.263 0.793 Gendermale -0.343 0.709 0.340 0.364 1.381 -1.010 0.312 RaceBlack -0.687 0.503 1.565 0.023 10.811 -0.439 0.661 RaceWhite -1.292 0.275 1.095 0.032 2.350 -1.180 0.238 Stage2 -0.329 0.720 0.463 0.290 1.785 -0.710 0.478 Stage3 -0.177 0.838 0.416 0.371 1.893 -0.425 0.671 Stage4 -0.253 0.776 0.474 0.307 1.963 -0.535 0.592 Purity -0.518 0.596 0.570 0.195 1.820 -0.909 0.363 Rsquare = 0.135 (max possible = 9.98e-01 ) Likelihood ratio test p = 1.98e-01 Wald test p = 1.51e-01 Score (logrank) test p = 1.53e-01 ACAT2 in OV (n=303): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.087 1.091 0.113 0.875 1.360 0.775 0.438 Age 0.036 1.037 0.008 1.020 1.053 4.431 0.000 *** RaceBlack -0.016 0.984 0.578 0.317 3.056 -0.027 0.978 RaceWhite -0.139 0.870 0.516 0.317 2.391 -0.269 0.788 Purity -0.613 0.542 0.677 0.144 2.042 -0.906 0.365 Rsquare = 0.084 (max possible = 9.97e-01 ) Likelihood ratio test p = 8.94e-04 Wald test p = 7.52e-04 Score (logrank) test p = 6.09e-04 ACAT2 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.060 0.942 0.167 0.679 1.307 -0.357 0.721 Age 0.022 1.023 0.011 1.001 1.045 2.045 0.041 * Gendermale -0.219 0.803 0.217 0.525 1.228 -1.011 0.312 RaceBlack -0.031 0.969 0.738 0.228 4.119 -0.042 0.967 RaceWhite 0.353 1.424 0.473 0.563 3.599 0.747 0.455 Stage2 0.638 1.893 0.439 0.800 4.477 1.452 0.146 Stage3 -0.241 0.786 1.091 0.093 6.677 -0.220 0.826 Stage4 0.291 1.337 0.837 0.260 6.891 0.347 0.728 Purity -0.705 0.494 0.419 0.217 1.124 -1.682 0.093 · Rsquare = 0.089 (max possible = 9.91e-01 ) Likelihood ratio test p = 8.02e-02 Wald test p = 1.16e-01 Score (logrank) test p = 1.11e-01 ACAT2 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.116 1.123 0.552 0.381 3.313 0.211 0.833 Age 0.037 1.038 0.028 0.982 1.097 1.319 0.187 Gendermale 1.387 4.004 0.897 0.690 23.251 1.546 0.122 RaceBlack -0.277 0.758 19793.529 0.000 Inf 0.000 1.000 RaceWhite 17.224 30204960.422 15989.417 0.000 Inf 0.001 0.999 Purity 5.643 282.216 3.402 0.358 222213.017 1.658 0.097 · Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.59e-01 Wald test p = 4.01e-01 Score (logrank) test p = 2.99e-01 ACAT2 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.425 1.529 0.473 0.605 3.865 0.898 0.369 Age 0.014 1.014 0.057 0.906 1.134 0.240 0.810 RaceBlack 16.122 10040278.445 10773.815 0.000 Inf 0.001 0.999 RaceWhite 17.481 39067074.424 10773.815 0.000 Inf 0.002 0.999 Purity 1.216 3.374 1.397 0.218 52.126 0.871 0.384 Rsquare = 0.009 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.23e-01 Wald test p = 7.54e-01 Score (logrank) test p = 6.94e-01 ACAT2 in READ (n=166): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.265 1.304 0.515 0.475 3.575 0.515 0.606 Age 0.120 1.128 0.050 1.023 1.243 2.425 0.015 * Gendermale -0.433 0.648 0.709 0.161 2.603 -0.611 0.541 RaceBlack 12.593 294593.754 10227.057 0.000 Inf 0.001 0.999 RaceWhite 11.654 115114.651 10227.057 0.000 Inf 0.001 0.999 Stage2 -1.897 0.150 1.271 0.012 1.809 -1.493 0.135 Stage3 -0.430 0.650 0.912 0.109 3.886 -0.472 0.637 Stage4 -0.201 0.818 0.964 0.124 5.407 -0.209 0.835 Purity 0.134 1.143 1.338 0.083 15.748 0.100 0.920 Rsquare = 0.212 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.39e-02 Wald test p = 2.64e-01 Score (logrank) test p = 4.91e-02 ACAT2 in SARC (n=260): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.017 1.017 0.121 0.802 1.290 0.141 0.888 Age 0.023 1.023 0.008 1.006 1.040 2.717 0.007 ** Gendermale -0.001 0.999 0.230 0.636 1.567 -0.006 0.995 RaceBlack -0.122 0.885 1.086 0.105 7.439 -0.113 0.910 RaceWhite -0.465 0.628 1.022 0.085 4.660 -0.455 0.649 Purity 0.928 2.529 0.590 0.796 8.035 1.573 0.116 Rsquare = 0.043 (max possible = 9.75e-01 ) Likelihood ratio test p = 1.19e-01 Wald test p = 1.6e-01 Score (logrank) test p = 1.6e-01 ACAT2 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.004 0.996 0.081 0.849 1.167 -0.055 0.956 Age 0.018 1.019 0.005 1.008 1.029 3.536 0.000 *** Gendermale -0.049 0.952 0.158 0.698 1.298 -0.312 0.755 RaceWhite -1.286 0.276 0.401 0.126 0.607 -3.203 0.001 ** Stage2 0.276 1.317 0.219 0.858 2.022 1.261 0.207 Stage3 0.610 1.841 0.205 1.232 2.749 2.981 0.003 ** Stage4 1.349 3.852 0.353 1.929 7.694 3.821 0.000 *** Purity 1.016 2.762 0.345 1.405 5.431 2.946 0.003 ** Rsquare = 0.123 (max possible = 9.92e-01 ) Likelihood ratio test p = 2.23e-08 Wald test p = 1.17e-08 Score (logrank) test p = 1.39e-09 ACAT2 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.280 1.323000e+00 0.264 0.789 2.217 1.062 0.288 Age 0.012 1.012000e+00 0.016 0.982 1.044 0.792 0.428 Gendermale 0.212 1.237000e+00 0.437 0.525 2.912 0.486 0.627 RaceWhite -1.129 3.230000e-01 0.633 0.093 1.118 -1.783 0.075 · Stage2 17.536 4.129595e+07 6245.223 0.000 Inf 0.003 0.998 Stage3 18.330 9.137157e+07 6245.223 0.000 Inf 0.003 0.998 Stage4 20.083 5.273429e+08 6245.223 0.000 Inf 0.003 0.997 Purity 0.172 1.187000e+00 0.945 0.186 7.566 0.182 0.856 Rsquare = 0.157 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.16e-02 Wald test p = 4.69e-02 Score (logrank) test p = 3.31e-03 ACAT2 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.029 0.971 0.088 0.818 1.154 -0.331 0.741 Age 0.020 1.021 0.006 1.009 1.032 3.624 0.000 *** Gendermale -0.051 0.950 0.173 0.676 1.335 -0.295 0.768 RaceWhite -1.048 0.351 0.600 0.108 1.138 -1.745 0.081 · Stage2 0.155 1.168 0.231 0.743 1.835 0.673 0.501 Stage3 0.559 1.749 0.209 1.161 2.635 2.672 0.008 ** Stage4 1.120 3.066 0.402 1.395 6.736 2.790 0.005 ** Purity 1.120 3.066 0.377 1.465 6.415 2.974 0.003 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 1.05e-06 Wald test p = 1.5e-06 Score (logrank) test p = 5.84e-07 ACAT2 in STAD (n=415): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -0.207 0.813 0.125 0.636 1.040 -1.649 0.099 · Age 0.030 1.030 0.010 1.009 1.052 2.834 0.005 ** Gendermale 0.156 1.169 0.208 0.777 1.758 0.748 0.455 RaceBlack 0.295 1.343 0.448 0.558 3.232 0.659 0.510 RaceWhite 0.095 1.099 0.244 0.681 1.773 0.388 0.698 Stage2 0.491 1.633 0.390 0.761 3.508 1.258 0.208 Stage3 0.916 2.500 0.364 1.226 5.100 2.519 0.012 * Stage4 1.390 4.017 0.505 1.493 10.809 2.753 0.006 ** Purity -0.466 0.627 0.380 0.298 1.321 -1.228 0.220 Rsquare = 0.078 (max possible = 9.79e-01 ) Likelihood ratio test p = 5.18e-03 Wald test p = 7.62e-03 Score (logrank) test p = 5.87e-03 ACAT2 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -4.071 1.700000e-02 37872.635 0 Inf 0.000 1.000 Age -1.953 1.420000e-01 1778.832 0 Inf -0.001 0.999 RaceBlack 10.427 3.374523e+04 21227409.513 0 Inf 0.000 1.000 RaceWhite -35.511 0.000000e+00 21204074.428 0 Inf 0.000 1.000 Stage2 -2.983 5.100000e-02 41314.533 0 Inf 0.000 1.000 Stage3 18.057 6.948037e+07 122889.465 0 Inf 0.000 1.000 Purity 19.945 4.592799e+08 203538.933 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 2.88e-03 ACAT2 in THCA (n=509): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.700 2.014 0.493 0.766 5.297 1.418 0.156 Age 0.150 1.162 0.029 1.098 1.231 5.172 0.000 *** Gendermale 0.198 1.219 0.637 0.350 4.247 0.312 0.755 RaceBlack 16.797 19720486.766 5866.635 0.000 Inf 0.003 0.998 RaceWhite 16.774 19277844.452 5866.635 0.000 Inf 0.003 0.998 Stage2 0.208 1.231 1.099 0.143 10.611 0.189 0.850 Stage3 0.203 1.226 0.843 0.235 6.398 0.241 0.809 Stage4 1.693 5.438 0.958 0.832 35.551 1.768 0.077 · Purity 2.286 9.834 1.072 1.203 80.363 2.133 0.033 * Rsquare = 0.154 (max possible = 3.47e-01 ) Likelihood ratio test p = 9.84e-11 Wald test p = 2.56e-04 Score (logrank) test p = 3.48e-11 ACAT2 in THYM (n=120): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 -1.319 0.267 0.522 0.096 0.744 -2.526 0.012 * Age 0.035 1.035 0.033 0.971 1.104 1.056 0.291 Gendermale -0.103 0.902 0.774 0.198 4.110 -0.133 0.894 RaceBlack -14.453 0.000 8009.111 0.000 Inf -0.002 0.999 RaceWhite 0.851 2.343 1.189 0.228 24.091 0.716 0.474 Purity 0.161 1.175 1.213 0.109 12.669 0.133 0.894 Rsquare = 0.099 (max possible = 4.51e-01 ) Likelihood ratio test p = 6.63e-02 Wald test p = 1.62e-01 Score (logrank) test p = 5.15e-02 ACAT2 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.611 1.843 0.174 1.311 2.591 3.516 0.000 *** Age 0.052 1.053 0.016 1.020 1.088 3.194 0.001 ** RaceBlack -0.663 0.515 0.796 0.108 2.454 -0.832 0.405 RaceWhite -0.665 0.514 0.745 0.119 2.214 -0.893 0.372 Purity 0.290 1.337 0.656 0.370 4.832 0.443 0.658 Rsquare = 0.077 (max possible = 7.81e-01 ) Likelihood ratio test p = 3.69e-04 Wald test p = 4.22e-04 Score (logrank) test p = 3.74e-04 ACAT2 in UCS (n=57): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.080 1.083 0.362 0.533 2.201 0.220 0.826 Age 0.045 1.046 0.025 0.996 1.099 1.810 0.070 · RaceBlack 17.641 45850842.268 6474.389 0.000 Inf 0.003 0.998 RaceWhite 17.896 59188174.057 6474.389 0.000 Inf 0.003 0.998 Purity -0.882 0.414 1.056 0.052 3.277 -0.835 0.403 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.49e-01 Wald test p = 3.56e-01 Score (logrank) test p = 2.62e-01 ACAT2 in UVM (n=80): Model: Surv(OS, EVENT) ~ `ACAT2` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACAT2 0.696 2.006 0.289 1.139 3.532 2.411 0.016 * Age 0.040 1.041 0.019 1.003 1.080 2.110 0.035 * Gendermale 0.291 1.338 0.480 0.522 3.428 0.607 0.544 Stage3 0.122 1.130 0.514 0.412 3.095 0.237 0.812 Stage4 3.588 36.144 1.208 3.388 385.543 2.970 0.003 ** Purity 2.411 11.149 1.344 0.801 155.246 1.795 0.073 · Rsquare = 0.31 (max possible = 8.72e-01 ) Likelihood ratio test p = 7.32e-05 Wald test p = 8.57e-04 Score (logrank) test p = 4.03e-10 ACLY in ACC (n=79): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.657 1.929 0.227 1.237 3.007 2.898 0.004 ** Age 0.017 1.017 0.014 0.988 1.046 1.150 0.250 Gendermale 1.067 2.908 0.506 1.079 7.835 2.110 0.035 * RaceBlack 0.420 1.522 13163.844 0.000 Inf 0.000 1.000 RaceWhite 17.231 30443637.023 11333.485 0.000 Inf 0.002 0.999 Purity 2.216 9.169 2.088 0.153 548.716 1.061 0.289 Rsquare = 0.185 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.13e-02 Wald test p = 1.08e-01 Score (logrank) test p = 6.62e-02 ACLY in BLCA (n=408): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.283 1.326 0.136 1.017 1.730 2.084 0.037 * Age 0.033 1.033 0.009 1.016 1.051 3.855 0.000 *** Gendermale -0.224 0.799 0.180 0.561 1.138 -1.244 0.213 RaceBlack 0.564 1.757 0.454 0.722 4.275 1.242 0.214 RaceWhite 0.012 1.012 0.358 0.501 2.042 0.032 0.974 Stage2 14.731 2498998.302 1865.878 0.000 Inf 0.008 0.994 Stage3 15.143 3772893.103 1865.878 0.000 Inf 0.008 0.994 Stage4 15.648 6247807.605 1865.878 0.000 Inf 0.008 0.993 Purity -0.036 0.965 0.352 0.484 1.925 -0.102 0.919 Rsquare = 0.141 (max possible = 9.91e-01 ) Likelihood ratio test p = 2.97e-08 Wald test p = 1.26e-07 Score (logrank) test p = 4.64e-08 ACLY in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.182 1.200 0.141 0.911 1.581 1.294 0.196 Age 0.036 1.036 0.008 1.021 1.052 4.725 0.000 *** Gendermale 0.002 1.002 1.008 0.139 7.217 0.002 0.999 RaceBlack -0.013 0.987 0.619 0.293 3.324 -0.020 0.984 RaceWhite -0.303 0.738 0.599 0.228 2.390 -0.506 0.613 Stage2 0.396 1.486 0.304 0.820 2.695 1.305 0.192 Stage3 1.178 3.247 0.313 1.758 5.997 3.762 0.000 *** Stage4 2.558 12.905 0.390 6.008 27.720 6.557 0.000 *** Purity 0.433 1.541 0.427 0.668 3.556 1.014 0.311 Rsquare = 0.083 (max possible = 7.85e-01 ) Likelihood ratio test p = 1.15e-12 Wald test p = 4.95e-16 Score (logrank) test p = 4.83e-22 ACLY in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.401 1.493000e+00 0.381 0.708 3.150 1.053 0.292 Age 0.006 1.006000e+00 0.018 0.971 1.042 0.343 0.731 RaceBlack -0.864 4.210000e-01 1.105 0.048 3.678 -0.782 0.434 RaceWhite -1.212 2.980000e-01 1.109 0.034 2.618 -1.092 0.275 Stage2 18.733 1.366179e+08 6438.456 0.000 Inf 0.003 0.998 Stage3 20.185 5.839840e+08 6438.456 0.000 Inf 0.003 0.997 Stage4 21.545 2.274373e+09 6438.456 0.000 Inf 0.003 0.997 Purity 0.702 2.018000e+00 0.960 0.307 13.250 0.731 0.465 Rsquare = 0.163 (max possible = 7.18e-01 ) Likelihood ratio test p = 3.25e-04 Wald test p = 4.81e-03 Score (logrank) test p = 2.94e-06 ACLY in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.649 5.220000e-01 0.576 0.169 1.615 -1.127 0.260 Age 0.027 1.028000e+00 0.029 0.972 1.087 0.952 0.341 RaceBlack -3.381 3.400000e-02 1.860 0.001 1.304 -1.817 0.069 · RaceWhite -1.619 1.980000e-01 1.467 0.011 3.515 -1.103 0.270 Stage2 18.105 7.294080e+07 14829.386 0.000 Inf 0.001 0.999 Stage3 20.062 5.160891e+08 14829.386 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.657 3.872800e+01 2.378 0.366 4097.699 1.537 0.124 Rsquare = 0.384 (max possible = 6.68e-01 ) Likelihood ratio test p = 1.74e-04 Wald test p = 2.15e-01 Score (logrank) test p = 8.8e-15 ACLY in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.239 1.270 0.223 0.820 1.968 1.070 0.284 Age 0.049 1.050 0.012 1.026 1.075 4.111 0.000 *** Gendermale -15.545 0.000 3477.295 0.000 Inf -0.004 0.996 RaceBlack -0.520 0.595 1.176 0.059 5.960 -0.442 0.658 RaceWhite 0.019 1.019 1.053 0.129 8.029 0.018 0.986 Stage2 0.283 1.327 0.376 0.635 2.775 0.752 0.452 Stage3 0.854 2.350 0.394 1.086 5.085 2.169 0.030 * Stage4 2.155 8.625 0.592 2.705 27.504 3.642 0.000 *** Purity 0.175 1.191 0.630 0.347 4.091 0.277 0.782 Rsquare = 0.072 (max possible = 6.81e-01 ) Likelihood ratio test p = 6.52e-05 Wald test p = 1.21e-05 Score (logrank) test p = 2.35e-07 ACLY in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.174 1.190 0.383 0.561 2.523 0.453 0.651 Age 0.052 1.053 0.021 1.010 1.098 2.420 0.016 * Gendermale 1.110 3.034 1.143 0.323 28.486 0.971 0.331 RaceBlack 16.678 17504525.189 6431.640 0.000 Inf 0.003 0.998 RaceWhite 16.039 9239135.087 6431.640 0.000 Inf 0.002 0.998 Stage2 0.613 1.847 1.084 0.221 15.455 0.566 0.571 Stage3 1.553 4.725 1.065 0.586 38.127 1.458 0.145 Stage4 2.145 8.543 1.176 0.853 85.575 1.825 0.068 · Purity 0.997 2.709 1.322 0.203 36.158 0.754 0.451 Rsquare = 0.106 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.93e-02 Wald test p = 6.81e-02 Score (logrank) test p = 2.26e-02 ACLY in CESC (n=306): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.556 1.744 0.183 1.218 2.497 3.037 0.002 ** Age 0.007 1.007 0.010 0.987 1.028 0.707 0.480 RaceBlack 1.115 3.049 1.068 0.376 24.749 1.044 0.297 RaceWhite 0.809 2.246 1.015 0.307 16.423 0.797 0.426 Purity 0.513 1.670 0.730 0.399 6.990 0.702 0.483 Rsquare = 0.052 (max possible = 8.91e-01 ) Likelihood ratio test p = 3.33e-02 Wald test p = 3.3e-02 Score (logrank) test p = 3.21e-02 ACLY in CHOL (n=36): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.070 1.073 0.422 0.469 2.451 0.166 0.868 Age 0.018 1.018 0.022 0.976 1.062 0.827 0.408 Gendermale 0.265 1.303 0.567 0.429 3.956 0.467 0.641 RaceBlack -0.370 0.691 1.493 0.037 12.905 -0.247 0.805 RaceWhite -1.042 0.353 0.908 0.060 2.089 -1.148 0.251 Stage2 0.664 1.943 0.673 0.520 7.268 0.987 0.324 Stage3 -15.399 0.000 6968.601 0.000 Inf -0.002 0.998 Stage4 0.800 2.226 0.682 0.584 8.479 1.173 0.241 Purity 2.104 8.201 1.610 0.349 192.560 1.307 0.191 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.78e-01 Wald test p = 6.45e-01 Score (logrank) test p = 4.81e-01 ACLY in COAD (n=458): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.288 0.750 0.272 0.440 1.277 -1.061 0.289 Age 0.024 1.024 0.012 1.001 1.047 2.062 0.039 * Gendermale 0.221 1.248 0.269 0.736 2.114 0.822 0.411 RaceBlack -0.451 0.637 0.835 0.124 3.271 -0.540 0.589 RaceWhite -0.496 0.609 0.780 0.132 2.809 -0.636 0.525 Stage2 0.187 1.206 0.563 0.400 3.635 0.332 0.740 Stage3 0.823 2.277 0.549 0.776 6.682 1.498 0.134 Stage4 1.956 7.068 0.556 2.377 21.016 3.517 0.000 *** Purity -0.185 0.831 0.600 0.256 2.693 -0.309 0.757 Rsquare = 0.113 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.02e-04 Wald test p = 1.01e-04 Score (logrank) test p = 1.43e-05 ACLY in DLBC (n=48): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 1.715 5.558 1.041 0.723 42.714 1.648 0.099 · Age -0.025 0.976 0.049 0.886 1.075 -0.498 0.619 Gendermale 0.326 1.385 1.110 0.157 12.197 0.294 0.769 RaceBlack 1.680 5.364 2.071 0.093 310.456 0.811 0.417 RaceWhite -3.433 0.032 1.752 0.001 1.001 -1.959 0.050 · Purity -3.469 0.031 2.755 0.000 6.897 -1.259 0.208 Rsquare = 0.213 (max possible = 5.58e-01 ) Likelihood ratio test p = 1.33e-01 Wald test p = 3.91e-01 Score (logrank) test p = 1.64e-01 ACLY in ESCA (n=185): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.006 1.006 0.192 0.690 1.466 0.029 0.977 Age 0.010 1.010 0.014 0.982 1.038 0.681 0.496 Gendermale 0.482 1.619 0.538 0.564 4.649 0.896 0.370 RaceBlack 0.337 1.401 1.068 0.173 11.364 0.316 0.752 RaceWhite -0.079 0.924 0.447 0.385 2.218 -0.177 0.860 Stage2 0.697 2.007 0.654 0.557 7.227 1.066 0.286 Stage3 1.455 4.283 0.671 1.151 15.942 2.169 0.030 * Stage4 2.860 17.468 0.779 3.797 80.360 3.673 0.000 *** Purity 0.212 1.237 0.777 0.270 5.668 0.273 0.785 Rsquare = 0.141 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.15e-02 Wald test p = 5.34e-03 Score (logrank) test p = 4.44e-04 ACLY in GBM (n=153): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.306 1.358 0.204 0.911 2.026 1.502 0.133 Age 0.030 1.030 0.008 1.014 1.047 3.643 0.000 *** Gendermale -0.045 0.956 0.216 0.627 1.459 -0.206 0.836 RaceBlack 0.620 1.858 0.729 0.445 7.754 0.850 0.395 RaceWhite -0.132 0.876 0.617 0.261 2.937 -0.214 0.831 Purity -1.528 0.217 0.599 0.067 0.702 -2.551 0.011 * Rsquare = 0.144 (max possible = 9.98e-01 ) Likelihood ratio test p = 1.9e-03 Wald test p = 2.16e-03 Score (logrank) test p = 1.9e-03 ACLY in HNSC (n=522): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.165 1.179 0.125 0.923 1.507 1.319 0.187 Age 0.021 1.021 0.008 1.006 1.037 2.792 0.005 ** Gendermale -0.266 0.767 0.173 0.547 1.075 -1.539 0.124 RaceBlack 0.107 1.113 0.559 0.372 3.329 0.192 0.848 RaceWhite -0.251 0.778 0.511 0.286 2.117 -0.492 0.623 Stage2 0.590 1.804 0.544 0.621 5.240 1.084 0.278 Stage3 0.860 2.364 0.537 0.826 6.767 1.604 0.109 Stage4 1.234 3.435 0.510 1.264 9.337 2.419 0.016 * Purity -0.053 0.948 0.365 0.464 1.938 -0.146 0.884 Rsquare = 0.073 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.6e-04 Wald test p = 6.58e-04 Score (logrank) test p = 4.71e-04 ACLY in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.761 2.141000e+00 0.363 1.052 4.358 2.099 0.036 * Age 0.003 1.003000e+00 0.025 0.955 1.053 0.102 0.919 Gendermale -0.379 6.850000e-01 0.560 0.229 2.051 -0.676 0.499 RaceBlack 18.677 1.292104e+08 12865.294 0.000 Inf 0.001 0.999 RaceWhite 17.839 5.587272e+07 12865.294 0.000 Inf 0.001 0.999 Stage2 17.117 2.714549e+07 5623.426 0.000 Inf 0.003 0.998 Stage3 16.784 1.946406e+07 5623.426 0.000 Inf 0.003 0.998 Stage4 17.523 4.075604e+07 5623.426 0.000 Inf 0.003 0.998 Purity -1.433 2.390000e-01 1.101 0.028 2.066 -1.301 0.193 Rsquare = 0.153 (max possible = 9.17e-01 ) Likelihood ratio test p = 2.89e-01 Wald test p = 5.51e-01 Score (logrank) test p = 4.03e-01 ACLY in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.038 1.039 0.137 0.795 1.359 0.280 0.779 Age 0.027 1.027 0.008 1.010 1.044 3.169 0.002 ** Gendermale -0.288 0.750 0.183 0.524 1.074 -1.572 0.116 RaceBlack -0.022 0.979 0.564 0.324 2.957 -0.038 0.969 RaceWhite -0.395 0.674 0.512 0.247 1.839 -0.771 0.441 Stage2 0.360 1.434 0.554 0.484 4.249 0.651 0.515 Stage3 0.728 2.072 0.541 0.718 5.980 1.347 0.178 Stage4 1.141 3.129 0.512 1.146 8.543 2.226 0.026 * Purity 0.202 1.224 0.401 0.558 2.686 0.505 0.614 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.3e-04 Wald test p = 9.18e-04 Score (logrank) test p = 6.93e-04 ACLY in KICH (n=66): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p ACLY 2.691 14.741 0.705 3.699 5.873600e+01 3.815 0.000 Age 0.051 1.053 0.028 0.996 1.113000e+00 1.812 0.070 Gendermale -1.726 0.178 0.748 0.041 7.700000e-01 -2.309 0.021 RaceBlack -15.959 0.000 3154.505 0.000 Inf -0.005 0.996 RaceWhite -1.492 0.225 1.169 0.023 2.221000e+00 -1.277 0.202 Stage2 14.187 1449748.915 0.865 266320.010 7.891904e+06 16.410 0.000 Stage3 15.519 5492711.011 0.792 1162972.172 2.594204e+07 19.593 0.000 Stage4 17.359 34586253.050 0.910 5807263.390 2.059850e+08 19.067 0.000 Purity 7.543 1886.832 3.986 0.764 4.662853e+06 1.892 0.058 signif ACLY *** Age · Gendermale * RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity · Rsquare = 0.427 (max possible = 6.71e-01 ) Likelihood ratio test p = 5.88e-05 Wald test p = 3.23e-219 Score (logrank) test p = 2.53e-09 ACLY in KIRC (n=533): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.220 0.802 0.094 0.667 0.965 -2.341 0.019 * Age 0.033 1.033 0.008 1.017 1.050 3.916 0.000 *** Gendermale -0.171 0.842 0.189 0.581 1.221 -0.905 0.365 RaceBlack 0.164 1.178 1.055 0.149 9.321 0.155 0.877 RaceWhite 0.185 1.203 1.014 0.165 8.784 0.182 0.855 Stage2 0.164 1.179 0.346 0.598 2.325 0.475 0.635 Stage3 0.772 2.164 0.230 1.377 3.400 3.350 0.001 ** Stage4 1.677 5.349 0.218 3.492 8.195 7.705 0.000 *** Purity -0.081 0.922 0.365 0.451 1.885 -0.222 0.825 Rsquare = 0.183 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.01e-15 Wald test p = 6.65e-16 Score (logrank) test p = 3.62e-19 ACLY in KIRP (n=290): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.552 1.737 0.284 0.995 3.033 1.942 0.052 · Age 0.016 1.016 0.016 0.985 1.049 0.997 0.319 Gendermale -0.459 0.632 0.384 0.298 1.342 -1.194 0.232 RaceBlack -1.818 0.162 1.212 0.015 1.746 -1.500 0.134 RaceWhite -2.018 0.133 1.189 0.013 1.368 -1.697 0.090 · Stage2 -0.548 0.578 1.055 0.073 4.572 -0.520 0.603 Stage3 1.686 5.399 0.431 2.320 12.565 3.912 0.000 *** Stage4 2.693 14.772 0.513 5.407 40.360 5.251 0.000 *** Purity -0.387 0.679 0.748 0.157 2.942 -0.517 0.605 Rsquare = 0.178 (max possible = 7.58e-01 ) Likelihood ratio test p = 2.13e-06 Wald test p = 1.2e-06 Score (logrank) test p = 1.7e-10 ACLY in LAML (n=173): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.401 1.493 0.302 0.827 2.696 1.330 0.184 Age 0.039 1.040 0.008 1.023 1.056 4.782 0.000 *** Gendermale -0.182 0.834 0.215 0.547 1.271 -0.846 0.398 RaceBlack -0.212 0.809 1.109 0.092 7.106 -0.191 0.848 RaceWhite -0.645 0.525 1.019 0.071 3.862 -0.633 0.527 Rsquare = 0.166 (max possible = 9.96e-01 ) Likelihood ratio test p = 5.49e-05 Wald test p = 1.77e-04 Score (logrank) test p = 1.16e-04 ACLY in LGG (n=516): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.299 1.349 0.204 0.904 2.012 1.465 0.143 Age 0.061 1.063 0.008 1.047 1.079 7.951 0.000 *** Gendermale 0.131 1.140 0.197 0.775 1.676 0.667 0.505 RaceBlack 15.503 5407054.256 2080.276 0.000 Inf 0.007 0.994 RaceWhite 15.459 5173488.138 2080.276 0.000 Inf 0.007 0.994 Purity -1.165 0.312 0.424 0.136 0.716 -2.747 0.006 ** Rsquare = 0.14 (max possible = 9.07e-01 ) Likelihood ratio test p = 4.11e-13 Wald test p = 6.6e-13 Score (logrank) test p = 3.12e-14 ACLY in LIHC (n=371): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.334 1.397 0.124 1.096 1.780 2.704 0.007 ** Age 0.013 1.013 0.008 0.997 1.029 1.550 0.121 Gendermale -0.065 0.937 0.230 0.596 1.471 -0.284 0.776 RaceBlack 0.875 2.398 0.491 0.916 6.279 1.781 0.075 · RaceWhite 0.030 1.031 0.238 0.646 1.644 0.127 0.899 Stage2 0.291 1.337 0.262 0.800 2.237 1.108 0.268 Stage3 0.867 2.380 0.236 1.499 3.777 3.677 0.000 *** Stage4 1.608 4.993 0.620 1.481 16.828 2.594 0.009 ** Purity 0.689 1.991 0.466 0.799 4.962 1.478 0.139 Rsquare = 0.106 (max possible = 9.66e-01 ) Likelihood ratio test p = 6.5e-05 Wald test p = 4.66e-05 Score (logrank) test p = 1.57e-05 ACLY in LUAD (n=515): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.049 1.050 0.123 0.825 1.336 0.397 0.691 Age 0.007 1.007 0.009 0.990 1.025 0.797 0.426 Gendermale 0.006 1.006 0.171 0.719 1.408 0.037 0.971 RaceBlack 16.124 10061423.380 1881.961 0.000 Inf 0.009 0.993 RaceWhite 16.297 11957729.360 1881.961 0.000 Inf 0.009 0.993 Stage2 0.857 2.356 0.202 1.586 3.501 4.242 0.000 *** Stage3 0.997 2.711 0.222 1.755 4.188 4.497 0.000 *** Stage4 1.006 2.734 0.334 1.421 5.259 3.012 0.003 ** Purity 0.598 1.818 0.343 0.928 3.563 1.742 0.082 · Rsquare = 0.097 (max possible = 9.74e-01 ) Likelihood ratio test p = 2.25e-06 Wald test p = 2.94e-05 Score (logrank) test p = 3.43e-06 ACLY in LUSC (n=501): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.156 1.169 0.126 0.912 1.497 1.232 0.218 Age 0.017 1.017 0.009 0.999 1.036 1.806 0.071 · Gendermale 0.400 1.491 0.195 1.017 2.186 2.047 0.041 * RaceBlack 0.023 1.024 0.601 0.315 3.324 0.039 0.969 RaceWhite -0.515 0.598 0.557 0.200 1.782 -0.923 0.356 Stage2 0.158 1.171 0.192 0.804 1.705 0.824 0.410 Stage3 0.604 1.830 0.214 1.203 2.785 2.822 0.005 ** Stage4 0.831 2.296 0.788 0.490 10.749 1.055 0.291 Purity -0.382 0.683 0.366 0.333 1.398 -1.044 0.297 Rsquare = 0.054 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.44e-02 Wald test p = 1.11e-02 Score (logrank) test p = 9.21e-03 ACLY in MESO (n=87): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.742 2.100 0.205 1.404 3.141 3.612 0.000 *** Age 0.015 1.016 0.016 0.984 1.048 0.947 0.343 Gendermale -0.467 0.627 0.338 0.323 1.216 -1.381 0.167 RaceBlack -1.209 0.298 1.570 0.014 6.479 -0.770 0.441 RaceWhite -1.108 0.330 1.064 0.041 2.659 -1.041 0.298 Stage2 -0.166 0.847 0.459 0.344 2.085 -0.360 0.719 Stage3 -0.140 0.870 0.411 0.388 1.947 -0.339 0.734 Stage4 -0.223 0.800 0.468 0.320 2.003 -0.477 0.634 Purity -0.504 0.604 0.565 0.200 1.828 -0.892 0.372 Rsquare = 0.197 (max possible = 9.98e-01 ) Likelihood ratio test p = 2.85e-02 Wald test p = 3.8e-02 Score (logrank) test p = 3.09e-02 ACLY in OV (n=303): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.230 1.258 0.152 0.934 1.695 1.510 0.131 Age 0.037 1.038 0.008 1.021 1.054 4.533 0.000 *** RaceBlack -0.112 0.894 0.578 0.288 2.775 -0.193 0.847 RaceWhite -0.236 0.790 0.519 0.286 2.182 -0.456 0.649 Purity -0.417 0.659 0.668 0.178 2.439 -0.625 0.532 Rsquare = 0.09 (max possible = 9.97e-01 ) Likelihood ratio test p = 4.31e-04 Wald test p = 3.83e-04 Score (logrank) test p = 3.05e-04 ACLY in PAAD (n=179): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.111 0.895 0.190 0.617 1.298 -0.585 0.558 Age 0.020 1.021 0.011 0.998 1.043 1.803 0.071 · Gendermale -0.237 0.789 0.220 0.512 1.214 -1.079 0.280 RaceBlack 0.050 1.052 0.748 0.243 4.556 0.067 0.946 RaceWhite 0.405 1.499 0.479 0.586 3.835 0.844 0.399 Stage2 0.578 1.782 0.444 0.747 4.251 1.302 0.193 Stage3 -0.342 0.710 1.105 0.081 6.193 -0.310 0.757 Stage4 0.238 1.268 0.824 0.252 6.373 0.289 0.773 Purity -0.701 0.496 0.413 0.221 1.115 -1.697 0.090 · Rsquare = 0.09 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.52e-02 Wald test p = 1.1e-01 Score (logrank) test p = 1.05e-01 ACLY in PCPG (n=181): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.665 1.944 0.568 0.638 5.918 1.170 0.242 Age 0.038 1.039 0.028 0.984 1.098 1.368 0.171 Gendermale 1.245 3.474 0.906 0.589 20.500 1.375 0.169 RaceBlack -0.405 0.667 19743.458 0.000 Inf 0.000 1.000 RaceWhite 16.994 24013884.865 15280.152 0.000 Inf 0.001 0.999 Purity 5.689 295.482 3.432 0.354 246389.861 1.658 0.097 · Rsquare = 0.063 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.01e-01 Wald test p = 2.62e-01 Score (logrank) test p = 1.89e-01 ACLY in PRAD (n=498): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.812 2.252 0.541 0.780 6.500 1.501 0.133 Age 0.020 1.020 0.055 0.916 1.136 0.366 0.715 RaceBlack 15.240 4157273.652 6747.209 0.000 Inf 0.002 0.998 RaceWhite 16.378 12969401.399 6747.209 0.000 Inf 0.002 0.998 Purity 0.899 2.458 1.361 0.171 35.390 0.661 0.509 Rsquare = 0.013 (max possible = 1.83e-01 ) Likelihood ratio test p = 3.76e-01 Wald test p = 5.17e-01 Score (logrank) test p = 4.47e-01 ACLY in READ (n=166): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.051 0.951 0.547 0.325 2.778 -0.092 0.926 Age 0.108 1.114 0.046 1.018 1.220 2.347 0.019 * Gendermale -0.346 0.707 0.692 0.182 2.747 -0.500 0.617 RaceBlack 13.512 738474.566 10451.923 0.000 Inf 0.001 0.999 RaceWhite 12.498 267886.769 10451.923 0.000 Inf 0.001 0.999 Stage2 -1.855 0.156 1.255 0.013 1.831 -1.478 0.139 Stage3 -0.504 0.604 0.938 0.096 3.798 -0.537 0.591 Stage4 -0.157 0.855 0.960 0.130 5.607 -0.163 0.870 Purity 0.081 1.085 1.440 0.065 18.240 0.056 0.955 Rsquare = 0.209 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.69e-02 Wald test p = 2.31e-01 Score (logrank) test p = 4.17e-02 ACLY in SARC (n=260): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.404 1.498 0.167 1.081 2.076 2.426 0.015 * Age 0.020 1.020 0.008 1.004 1.037 2.417 0.016 * Gendermale -0.052 0.950 0.223 0.613 1.470 -0.232 0.816 RaceBlack 0.019 1.020 1.089 0.121 8.615 0.018 0.986 RaceWhite -0.359 0.698 1.022 0.094 5.173 -0.352 0.725 Purity 1.004 2.729 0.588 0.862 8.632 1.708 0.088 · Rsquare = 0.067 (max possible = 9.75e-01 ) Likelihood ratio test p = 1.35e-02 Wald test p = 1.98e-02 Score (logrank) test p = 1.9e-02 ACLY in SKCM (n=471): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.083 0.920 0.126 0.719 1.179 -0.657 0.511 Age 0.019 1.019 0.005 1.009 1.029 3.584 0.000 *** Gendermale -0.053 0.948 0.157 0.697 1.291 -0.337 0.736 RaceWhite -1.277 0.279 0.402 0.127 0.613 -3.179 0.001 ** Stage2 0.283 1.327 0.219 0.864 2.038 1.293 0.196 Stage3 0.607 1.836 0.204 1.230 2.739 2.975 0.003 ** Stage4 1.342 3.826 0.352 1.920 7.622 3.816 0.000 *** Purity 1.048 2.852 0.342 1.458 5.577 3.061 0.002 ** Rsquare = 0.124 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.85e-08 Wald test p = 9.67e-09 Score (logrank) test p = 1.17e-09 ACLY in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.334 1.397000e+00 0.331 0.730 2.673 1.009 0.313 Age 0.012 1.012000e+00 0.016 0.981 1.044 0.771 0.441 Gendermale 0.177 1.194000e+00 0.434 0.510 2.794 0.408 0.684 RaceWhite -1.198 3.020000e-01 0.631 0.088 1.040 -1.898 0.058 · Stage2 17.504 3.999103e+07 6234.262 0.000 Inf 0.003 0.998 Stage3 18.077 7.094255e+07 6234.262 0.000 Inf 0.003 0.998 Stage4 20.139 5.576115e+08 6234.262 0.000 Inf 0.003 0.997 Purity -0.172 8.420000e-01 1.036 0.111 6.409 -0.166 0.868 Rsquare = 0.156 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.28e-02 Wald test p = 4.21e-02 Score (logrank) test p = 3.13e-03 ACLY in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.103 0.902 0.142 0.683 1.192 -0.727 0.467 Age 0.021 1.021 0.006 1.010 1.033 3.703 0.000 *** Gendermale -0.065 0.937 0.172 0.668 1.313 -0.379 0.705 RaceWhite -1.038 0.354 0.600 0.109 1.149 -1.729 0.084 · Stage2 0.167 1.181 0.231 0.751 1.859 0.721 0.471 Stage3 0.560 1.750 0.209 1.162 2.636 2.676 0.007 ** Stage4 1.118 3.057 0.400 1.396 6.694 2.795 0.005 ** Purity 1.172 3.228 0.371 1.560 6.679 3.158 0.002 ** Rsquare = 0.135 (max possible = 9.95e-01 ) Likelihood ratio test p = 8.78e-07 Wald test p = 1.35e-06 Score (logrank) test p = 5.14e-07 ACLY in STAD (n=415): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.115 1.122 0.125 0.879 1.433 0.923 0.356 Age 0.026 1.027 0.010 1.006 1.047 2.566 0.010 * Gendermale 0.089 1.093 0.211 0.723 1.653 0.421 0.674 RaceBlack 0.213 1.237 0.451 0.511 2.993 0.472 0.637 RaceWhite 0.076 1.079 0.245 0.668 1.742 0.309 0.757 Stage2 0.475 1.608 0.390 0.748 3.456 1.217 0.224 Stage3 0.896 2.451 0.365 1.199 5.009 2.458 0.014 * Stage4 1.335 3.801 0.504 1.414 10.214 2.647 0.008 ** Purity -0.574 0.563 0.382 0.266 1.190 -1.505 0.132 Rsquare = 0.072 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.03e-02 Wald test p = 1.4e-02 Score (logrank) test p = 1.15e-02 ACLY in TGCT (n=150): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -5.528 4.000000e-03 41577.678 0 Inf 0.000 1.000 Age -1.747 1.740000e-01 1681.316 0 Inf -0.001 0.999 RaceBlack -1.517 2.190000e-01 12741337.961 0 Inf 0.000 1.000 RaceWhite -49.699 0.000000e+00 21166074.604 0 Inf 0.000 1.000 Stage2 -0.054 9.480000e-01 41927.041 0 Inf 0.000 1.000 Stage3 23.061 1.035346e+10 120885.231 0 Inf 0.000 1.000 Purity 27.805 1.189775e+12 199103.045 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.11e-03 ACLY in THCA (n=509): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.749 2.115 0.484 0.819 5.461 1.547 0.122 Age 0.146 1.157 0.028 1.096 1.222 5.274 0.000 *** Gendermale -0.257 0.773 0.654 0.214 2.788 -0.393 0.694 RaceBlack 17.563 42411634.658 9172.139 0.000 Inf 0.002 0.998 RaceWhite 17.383 35428149.560 9172.139 0.000 Inf 0.002 0.998 Stage2 0.701 2.015 1.156 0.209 19.432 0.606 0.545 Stage3 0.374 1.454 0.862 0.269 7.871 0.434 0.664 Stage4 1.888 6.605 0.992 0.946 46.132 1.904 0.057 · Purity 2.421 11.254 1.121 1.252 101.201 2.160 0.031 * Rsquare = 0.155 (max possible = 3.47e-01 ) Likelihood ratio test p = 7.81e-11 Wald test p = 5.2e-05 Score (logrank) test p = 3.96e-11 ACLY in THYM (n=120): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.825 0.438 0.463 0.177 1.087 -1.780 0.075 · Age 0.059 1.061 0.036 0.990 1.138 1.668 0.095 · Gendermale -0.172 0.842 0.765 0.188 3.775 -0.224 0.822 RaceBlack -16.505 0.000 11507.462 0.000 Inf -0.001 0.999 RaceWhite 0.361 1.435 1.095 0.168 12.264 0.330 0.742 Purity 0.434 1.544 1.152 0.161 14.768 0.377 0.706 Rsquare = 0.069 (max possible = 4.51e-01 ) Likelihood ratio test p = 2.31e-01 Wald test p = 3.76e-01 Score (logrank) test p = 2.26e-01 ACLY in UCEC (n=545): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.001 1.001 0.192 0.687 1.458 0.005 0.996 Age 0.050 1.051 0.016 1.019 1.084 3.163 0.002 ** RaceBlack -0.414 0.661 0.794 0.140 3.131 -0.521 0.602 RaceWhite -0.526 0.591 0.745 0.137 2.546 -0.706 0.480 Purity 0.447 1.563 0.646 0.441 5.542 0.691 0.489 Rsquare = 0.038 (max possible = 7.81e-01 ) Likelihood ratio test p = 5.03e-02 Wald test p = 5.93e-02 Score (logrank) test p = 5.77e-02 ACLY in UCS (n=57): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY -0.065 0.937 0.238 0.588 1.494 -0.272 0.785 Age 0.044 1.045 0.024 0.997 1.096 1.824 0.068 · RaceBlack 17.579 43103338.717 6475.034 0.000 Inf 0.003 0.998 RaceWhite 17.843 56141332.401 6475.034 0.000 Inf 0.003 0.998 Purity -0.902 0.406 1.064 0.050 3.267 -0.848 0.397 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.47e-01 Wald test p = 3.55e-01 Score (logrank) test p = 2.62e-01 ACLY in UVM (n=80): Model: Surv(OS, EVENT) ~ `ACLY` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif ACLY 0.968 2.633 0.421 1.155 6.004 2.302 0.021 * Age 0.045 1.046 0.019 1.008 1.086 2.394 0.017 * Gendermale 0.398 1.489 0.479 0.582 3.808 0.830 0.406 Stage3 0.189 1.208 0.508 0.447 3.266 0.372 0.710 Stage4 4.045 57.105 1.215 5.277 617.970 3.329 0.001 ** Purity 1.873 6.506 1.293 0.516 82.032 1.448 0.148 Rsquare = 0.308 (max possible = 8.72e-01 ) Likelihood ratio test p = 8.11e-05 Wald test p = 1.56e-03 Score (logrank) test p = 6.61e-10 CYP51A1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.208 1.231 0.150 0.918 1.652 1.388 0.165 Age 0.007 1.007 0.014 0.980 1.035 0.502 0.616 Gendermale 0.565 1.759 0.443 0.738 4.191 1.275 0.202 RaceBlack -0.343 0.709 12227.241 0.000 Inf 0.000 1.000 RaceWhite 16.563 15601459.762 10436.268 0.000 Inf 0.002 0.999 Purity 2.195 8.980 2.304 0.098 820.649 0.953 0.341 Rsquare = 0.095 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.8e-01 Wald test p = 6.67e-01 Score (logrank) test p = 5.04e-01 CYP51A1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.288 1.334 0.120 1.054 1.689 2.397 0.017 * Age 0.033 1.033 0.009 1.016 1.051 3.845 0.000 *** Gendermale -0.165 0.848 0.179 0.598 1.204 -0.921 0.357 RaceBlack 0.548 1.730 0.452 0.714 4.192 1.214 0.225 RaceWhite 0.101 1.106 0.354 0.552 2.215 0.285 0.776 Stage2 14.303 1628303.796 1877.220 0.000 Inf 0.008 0.994 Stage3 14.849 2810361.560 1877.220 0.000 Inf 0.008 0.994 Stage4 15.276 4308804.714 1877.220 0.000 Inf 0.008 0.994 Purity 0.070 1.073 0.344 0.547 2.103 0.204 0.838 Rsquare = 0.145 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.59e-08 Wald test p = 1.33e-07 Score (logrank) test p = 3.87e-08 CYP51A1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.085 0.919 0.128 0.715 1.181 -0.661 0.509 Age 0.036 1.037 0.008 1.021 1.052 4.753 0.000 *** Gendermale 0.025 1.026 1.008 0.142 7.393 0.025 0.980 RaceBlack 0.002 1.002 0.619 0.298 3.370 0.003 0.998 RaceWhite -0.192 0.825 0.597 0.256 2.661 -0.322 0.748 Stage2 0.411 1.508 0.304 0.832 2.736 1.353 0.176 Stage3 1.189 3.285 0.313 1.779 6.065 3.801 0.000 *** Stage4 2.548 12.788 0.392 5.934 27.556 6.506 0.000 *** Purity 0.572 1.771 0.429 0.765 4.102 1.334 0.182 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.04e-12 Wald test p = 6.08e-16 Score (logrank) test p = 7.54e-22 CYP51A1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.248 7.810000e-01 0.328 0.410 1.486 -0.754 0.451 Age 0.013 1.013000e+00 0.018 0.978 1.049 0.714 0.475 RaceBlack -0.825 4.380000e-01 1.116 0.049 3.909 -0.739 0.460 RaceWhite -1.075 3.410000e-01 1.139 0.037 3.184 -0.944 0.345 Stage2 18.679 1.295038e+08 6499.081 0.000 Inf 0.003 0.998 Stage3 20.118 5.461175e+08 6499.081 0.000 Inf 0.003 0.998 Stage4 21.294 1.769632e+09 6499.081 0.000 Inf 0.003 0.997 Purity 0.874 2.395000e+00 0.972 0.357 16.092 0.899 0.369 Rsquare = 0.16 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.14e-04 Wald test p = 6.66e-03 Score (logrank) test p = 4.05e-06 CYP51A1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.413 1.511000e+00 0.482 0.588 3.884 0.857 0.391 Age 0.022 1.022000e+00 0.031 0.963 1.085 0.711 0.477 RaceBlack -2.788 6.200000e-02 1.864 0.002 2.375 -1.496 0.135 RaceWhite -1.341 2.610000e-01 1.525 0.013 5.197 -0.879 0.379 Stage2 18.237 8.322358e+07 15337.158 0.000 Inf 0.001 0.999 Stage3 19.832 4.100815e+08 15337.158 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.042 2.093800e+01 2.342 0.212 2064.753 1.298 0.194 Rsquare = 0.378 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.18e-04 Wald test p = 2.22e-01 Score (logrank) test p = 9.44e-15 CYP51A1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.086 1.089 0.216 0.714 1.663 0.397 0.692 Age 0.048 1.050 0.012 1.025 1.074 4.064 0.000 *** Gendermale -15.319 0.000 3460.157 0.000 Inf -0.004 0.996 RaceBlack -0.485 0.616 1.181 0.061 6.228 -0.411 0.681 RaceWhite 0.187 1.206 1.043 0.156 9.303 0.179 0.858 Stage2 0.308 1.360 0.377 0.649 2.849 0.816 0.415 Stage3 0.862 2.368 0.394 1.095 5.122 2.191 0.028 * Stage4 2.151 8.596 0.591 2.697 27.396 3.638 0.000 *** Purity 0.254 1.289 0.631 0.374 4.438 0.403 0.687 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 9.78e-05 Wald test p = 1.82e-05 Score (logrank) test p = 3.63e-07 CYP51A1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.291 0.747 0.254 0.454 1.231 -1.145 0.252 Age 0.047 1.048 0.020 1.007 1.091 2.283 0.022 * Gendermale 0.963 2.620 1.113 0.296 23.222 0.865 0.387 RaceBlack 16.596 16123026.040 7017.610 0.000 Inf 0.002 0.998 RaceWhite 16.080 9624540.392 7017.610 0.000 Inf 0.002 0.998 Stage2 0.696 2.007 1.072 0.245 16.406 0.650 0.516 Stage3 1.586 4.882 1.061 0.611 39.030 1.495 0.135 Stage4 2.284 9.820 1.182 0.969 99.535 1.933 0.053 · Purity 1.175 3.238 1.316 0.245 42.721 0.893 0.372 Rsquare = 0.112 (max possible = 6.98e-01 ) Likelihood ratio test p = 2.81e-02 Wald test p = 4.57e-02 Score (logrank) test p = 1.48e-02 CYP51A1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.235 1.265 0.184 0.883 1.814 1.281 0.200 Age 0.011 1.011 0.010 0.992 1.031 1.132 0.258 RaceBlack 1.011 2.747 1.068 0.339 22.268 0.947 0.344 RaceWhite 0.820 2.270 1.015 0.311 16.589 0.808 0.419 Purity 0.511 1.667 0.735 0.395 7.040 0.696 0.487 Rsquare = 0.021 (max possible = 8.91e-01 ) Likelihood ratio test p = 4.44e-01 Wald test p = 4.69e-01 Score (logrank) test p = 4.61e-01 CYP51A1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -1.166 0.311 0.761 0.070 1.383 -1.533 0.125 Age 0.019 1.019 0.022 0.975 1.065 0.850 0.395 Gendermale 0.195 1.215 0.552 0.412 3.587 0.353 0.724 RaceBlack 0.764 2.148 1.675 0.081 57.263 0.456 0.648 RaceWhite -0.743 0.475 0.923 0.078 2.902 -0.806 0.420 Stage2 1.009 2.744 0.708 0.685 10.989 1.426 0.154 Stage3 -19.341 0.000 7239.360 0.000 Inf -0.003 0.998 Stage4 1.176 3.240 0.690 0.839 12.517 1.705 0.088 · Purity 0.545 1.724 1.864 0.045 66.564 0.292 0.770 Rsquare = 0.266 (max possible = 9.46e-01 ) Likelihood ratio test p = 2.65e-01 Wald test p = 4.81e-01 Score (logrank) test p = 2.62e-01 CYP51A1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.243 0.784 0.197 0.532 1.154 -1.234 0.217 Age 0.024 1.024 0.011 1.001 1.047 2.055 0.040 * Gendermale 0.237 1.267 0.272 0.744 2.158 0.871 0.384 RaceBlack -0.514 0.598 0.830 0.118 3.043 -0.619 0.536 RaceWhite -0.562 0.570 0.781 0.123 2.635 -0.719 0.472 Stage2 0.173 1.188 0.563 0.394 3.584 0.306 0.759 Stage3 0.793 2.211 0.549 0.754 6.481 1.446 0.148 Stage4 1.864 6.448 0.552 2.186 19.017 3.377 0.001 ** Purity -0.348 0.706 0.598 0.219 2.283 -0.581 0.561 Rsquare = 0.114 (max possible = 9.04e-01 ) Likelihood ratio test p = 2.58e-04 Wald test p = 7.45e-05 Score (logrank) test p = 1.17e-05 CYP51A1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.940 2.560 0.931 0.413 15.860 1.010 0.312 Age -0.019 0.981 0.047 0.895 1.075 -0.407 0.684 Gendermale 0.844 2.327 1.076 0.282 19.173 0.785 0.433 RaceBlack 0.694 2.001 1.711 0.070 57.270 0.405 0.685 RaceWhite -2.387 0.092 1.392 0.006 1.405 -1.715 0.086 · Purity -2.581 0.076 2.158 0.001 5.203 -1.196 0.232 Rsquare = 0.154 (max possible = 5.58e-01 ) Likelihood ratio test p = 3.34e-01 Wald test p = 5.34e-01 Score (logrank) test p = 2.9e-01 CYP51A1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.081 0.922 0.186 0.640 1.329 -0.434 0.664 Age 0.010 1.010 0.014 0.982 1.038 0.685 0.493 Gendermale 0.491 1.634 0.538 0.569 4.688 0.913 0.361 RaceBlack 0.309 1.362 1.070 0.167 11.087 0.289 0.773 RaceWhite -0.095 0.909 0.447 0.378 2.185 -0.212 0.832 Stage2 0.652 1.919 0.661 0.525 7.005 0.986 0.324 Stage3 1.412 4.104 0.676 1.091 15.447 2.088 0.037 * Stage4 2.880 17.814 0.775 3.902 81.333 3.717 0.000 *** Purity 0.297 1.345 0.789 0.286 6.319 0.376 0.707 Rsquare = 0.142 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.08e-02 Wald test p = 5e-03 Score (logrank) test p = 4.13e-04 CYP51A1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.022 1.022 0.124 0.802 1.302 0.175 0.861 Age 0.029 1.030 0.008 1.013 1.047 3.492 0.000 *** Gendermale -0.089 0.914 0.216 0.599 1.395 -0.415 0.678 RaceBlack 0.508 1.663 0.735 0.394 7.018 0.692 0.489 RaceWhite -0.257 0.774 0.621 0.229 2.614 -0.413 0.680 Purity -1.089 0.337 0.533 0.118 0.958 -2.041 0.041 * Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.75e-03 Wald test p = 6.88e-03 Score (logrank) test p = 5.98e-03 CYP51A1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.157 1.170 0.091 0.979 1.399 1.722 0.085 · Age 0.020 1.020 0.008 1.005 1.036 2.605 0.009 ** Gendermale -0.245 0.783 0.172 0.558 1.098 -1.419 0.156 RaceBlack 0.020 1.021 0.563 0.338 3.077 0.036 0.971 RaceWhite -0.258 0.773 0.511 0.284 2.104 -0.504 0.614 Stage2 0.600 1.822 0.544 0.627 5.291 1.102 0.270 Stage3 0.842 2.321 0.537 0.811 6.645 1.569 0.117 Stage4 1.226 3.409 0.510 1.254 9.266 2.404 0.016 * Purity -0.118 0.889 0.368 0.432 1.828 -0.321 0.748 Rsquare = 0.076 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.64e-04 Wald test p = 4.49e-04 Score (logrank) test p = 3.19e-04 CYP51A1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.705 2.024000e+00 0.389 0.945 4.335 1.814 0.070 · Age -0.001 9.990000e-01 0.026 0.951 1.051 -0.022 0.982 Gendermale -0.320 7.260000e-01 0.550 0.247 2.134 -0.582 0.561 RaceBlack 19.282 2.365302e+08 12378.279 0.000 Inf 0.002 0.999 RaceWhite 18.520 1.104155e+08 12378.279 0.000 Inf 0.001 0.999 Stage2 17.174 2.874079e+07 5301.969 0.000 Inf 0.003 0.997 Stage3 16.852 2.083253e+07 5301.969 0.000 Inf 0.003 0.997 Stage4 17.312 3.299778e+07 5301.969 0.000 Inf 0.003 0.997 Purity -2.062 1.270000e-01 1.145 0.013 1.199 -1.801 0.072 · Rsquare = 0.136 (max possible = 9.17e-01 ) Likelihood ratio test p = 3.92e-01 Wald test p = 7.1e-01 Score (logrank) test p = 5.9e-01 CYP51A1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.109 1.115 0.097 0.923 1.349 1.129 0.259 Age 0.026 1.026 0.009 1.009 1.043 3.024 0.002 ** Gendermale -0.281 0.755 0.183 0.527 1.081 -1.534 0.125 RaceBlack -0.112 0.894 0.571 0.292 2.736 -0.196 0.845 RaceWhite -0.411 0.663 0.512 0.243 1.810 -0.802 0.423 Stage2 0.358 1.430 0.554 0.483 4.237 0.646 0.518 Stage3 0.715 2.044 0.541 0.708 5.904 1.322 0.186 Stage4 1.128 3.091 0.512 1.133 8.434 2.203 0.028 * Purity 0.153 1.165 0.405 0.526 2.578 0.377 0.706 Rsquare = 0.088 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.06e-04 Wald test p = 6.21e-04 Score (logrank) test p = 4.59e-04 CYP51A1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p CYP51A1 0.646 1.908 0.459 0.776 4.694000e+00 1.407 0.159 Age 0.075 1.078 0.029 1.018 1.141000e+00 2.564 0.010 Gendermale -0.875 0.417 0.727 0.100 1.733000e+00 -1.204 0.229 RaceBlack -14.473 0.000 2935.518 0.000 Inf -0.005 0.996 RaceWhite -0.719 0.487 1.156 0.051 4.691000e+00 -0.623 0.534 Stage2 14.179 1438632.692 0.847 273554.980 7.565806e+06 16.742 0.000 Stage3 15.160 3837759.363 0.778 834763.077 1.764380e+07 19.478 0.000 Stage4 17.671 47250768.848 0.898 8124620.695 2.747987e+08 19.672 0.000 Purity 0.373 1.452 3.300 0.002 9.349890e+02 0.113 0.910 signif CYP51A1 Age * Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.356 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.05e-03 Wald test p = 8.38e-222 Score (logrank) test p = 6.82e-09 CYP51A1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.246 0.782 0.117 0.622 0.984 -2.101 0.036 * Age 0.035 1.036 0.008 1.019 1.053 4.207 0.000 *** Gendermale -0.092 0.912 0.185 0.635 1.310 -0.501 0.617 RaceBlack 0.307 1.360 1.058 0.171 10.822 0.290 0.772 RaceWhite 0.298 1.347 1.018 0.183 9.912 0.293 0.770 Stage2 0.202 1.224 0.345 0.623 2.404 0.586 0.558 Stage3 0.698 2.010 0.236 1.264 3.195 2.952 0.003 ** Stage4 1.649 5.202 0.222 3.368 8.034 7.433 0.000 *** Purity 0.058 1.060 0.364 0.519 2.164 0.159 0.874 Rsquare = 0.182 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.41e-15 Wald test p = 8.74e-16 Score (logrank) test p = 4.99e-19 CYP51A1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.499 1.647 0.267 0.975 2.781 1.866 0.062 · Age 0.012 1.012 0.015 0.982 1.043 0.777 0.437 Gendermale -0.431 0.650 0.385 0.305 1.382 -1.120 0.263 RaceBlack -1.911 0.148 1.192 0.014 1.531 -1.603 0.109 RaceWhite -1.976 0.139 1.179 0.014 1.398 -1.676 0.094 · Stage2 -0.324 0.723 1.055 0.091 5.721 -0.307 0.759 Stage3 1.743 5.716 0.435 2.436 13.414 4.006 0.000 *** Stage4 2.818 16.743 0.525 5.984 46.841 5.368 0.000 *** Purity -0.351 0.704 0.737 0.166 2.985 -0.476 0.634 Rsquare = 0.177 (max possible = 7.58e-01 ) Likelihood ratio test p = 2.53e-06 Wald test p = 2.58e-06 Score (logrank) test p = 2.54e-10 CYP51A1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.318 1.374 0.149 1.026 1.840 2.130 0.033 * Age 0.038 1.038 0.008 1.022 1.055 4.668 0.000 *** Gendermale -0.176 0.839 0.213 0.553 1.272 -0.828 0.408 RaceBlack -0.066 0.937 1.111 0.106 8.257 -0.059 0.953 RaceWhite -0.612 0.543 1.018 0.074 3.993 -0.600 0.548 Rsquare = 0.182 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.54e-05 Wald test p = 5.44e-05 Score (logrank) test p = 3.58e-05 CYP51A1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.024 1.025 0.144 0.772 1.360 0.168 0.867 Age 0.062 1.064 0.008 1.048 1.080 8.043 0.000 *** Gendermale 0.097 1.102 0.200 0.745 1.630 0.485 0.628 RaceBlack 15.377 4763838.723 1985.993 0.000 Inf 0.008 0.994 RaceWhite 15.408 4916953.773 1985.993 0.000 Inf 0.008 0.994 Purity -0.960 0.383 0.403 0.174 0.844 -2.382 0.017 * Rsquare = 0.136 (max possible = 9.07e-01 ) Likelihood ratio test p = 1.13e-12 Wald test p = 1.07e-12 Score (logrank) test p = 9.44e-14 CYP51A1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.045 0.956 0.105 0.779 1.174 -0.427 0.669 Age 0.011 1.011 0.008 0.995 1.027 1.382 0.167 Gendermale -0.142 0.868 0.225 0.559 1.348 -0.631 0.528 RaceBlack 0.913 2.492 0.491 0.952 6.518 1.861 0.063 · RaceWhite 0.000 1.000 0.237 0.629 1.590 0.000 1.000 Stage2 0.303 1.354 0.262 0.810 2.264 1.157 0.247 Stage3 0.965 2.625 0.237 1.649 4.181 4.066 0.000 *** Stage4 1.596 4.934 0.619 1.468 16.584 2.580 0.010 * Purity 0.620 1.860 0.467 0.744 4.646 1.328 0.184 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.12e-03 Wald test p = 6.96e-04 Score (logrank) test p = 2.59e-04 CYP51A1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.133 1.143 0.121 0.902 1.448 1.103 0.270 Age 0.007 1.007 0.009 0.989 1.025 0.739 0.460 Gendermale 0.018 1.018 0.169 0.731 1.418 0.108 0.914 RaceBlack 16.163 10463663.300 1873.447 0.000 Inf 0.009 0.993 RaceWhite 16.329 12345814.978 1873.447 0.000 Inf 0.009 0.993 Stage2 0.856 2.354 0.201 1.586 3.494 4.250 0.000 *** Stage3 1.005 2.731 0.218 1.781 4.187 4.607 0.000 *** Stage4 1.011 2.749 0.334 1.429 5.288 3.030 0.002 ** Purity 0.557 1.746 0.344 0.889 3.427 1.619 0.105 Rsquare = 0.099 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.43e-06 Wald test p = 2.16e-05 Score (logrank) test p = 2.47e-06 CYP51A1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.021 0.980 0.106 0.795 1.207 -0.193 0.847 Age 0.016 1.016 0.009 0.998 1.035 1.749 0.080 · Gendermale 0.440 1.553 0.194 1.061 2.273 2.262 0.024 * RaceBlack 0.011 1.011 0.606 0.308 3.316 0.018 0.986 RaceWhite -0.520 0.594 0.563 0.197 1.792 -0.924 0.356 Stage2 0.214 1.239 0.187 0.859 1.788 1.145 0.252 Stage3 0.604 1.830 0.214 1.202 2.785 2.820 0.005 ** Stage4 0.750 2.116 0.796 0.445 10.071 0.942 0.346 Purity -0.340 0.712 0.366 0.347 1.458 -0.930 0.352 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.39e-02 Wald test p = 1.85e-02 Score (logrank) test p = 1.6e-02 CYP51A1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.338 1.402 0.166 1.012 1.942 2.032 0.042 * Age 0.012 1.012 0.016 0.981 1.045 0.751 0.453 Gendermale -0.268 0.765 0.335 0.397 1.474 -0.800 0.424 RaceBlack -0.835 0.434 1.601 0.019 10.008 -0.521 0.602 RaceWhite -1.077 0.340 1.089 0.040 2.879 -0.989 0.323 Stage2 -0.117 0.889 0.467 0.356 2.222 -0.251 0.802 Stage3 -0.079 0.924 0.420 0.406 2.104 -0.189 0.850 Stage4 -0.100 0.905 0.473 0.358 2.287 -0.211 0.833 Purity -0.652 0.521 0.563 0.173 1.569 -1.160 0.246 Rsquare = 0.103 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.2e-01 Wald test p = 3.91e-01 Score (logrank) test p = 3.71e-01 CYP51A1 in OV (n=303): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.086 1.090 0.119 0.862 1.377 0.720 0.472 Age 0.037 1.038 0.008 1.021 1.054 4.502 0.000 *** RaceBlack -0.001 0.999 0.581 0.320 3.118 -0.002 0.998 RaceWhite -0.115 0.891 0.518 0.323 2.462 -0.222 0.824 Purity -0.500 0.607 0.674 0.162 2.272 -0.742 0.458 Rsquare = 0.083 (max possible = 9.97e-01 ) Likelihood ratio test p = 9.23e-04 Wald test p = 7.89e-04 Score (logrank) test p = 6.47e-04 CYP51A1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.179 1.196 0.162 0.870 1.644 1.102 0.270 Age 0.021 1.021 0.011 1.000 1.043 1.967 0.049 * Gendermale -0.236 0.790 0.219 0.515 1.213 -1.077 0.281 RaceBlack 0.008 1.008 0.739 0.237 4.288 0.010 0.992 RaceWhite 0.419 1.520 0.479 0.594 3.887 0.874 0.382 Stage2 0.603 1.828 0.439 0.774 4.320 1.375 0.169 Stage3 -0.143 0.867 1.095 0.101 7.418 -0.130 0.896 Stage4 0.076 1.079 0.839 0.208 5.580 0.090 0.928 Purity -0.659 0.518 0.412 0.231 1.161 -1.597 0.110 Rsquare = 0.095 (max possible = 9.91e-01 ) Likelihood ratio test p = 5.69e-02 Wald test p = 8.34e-02 Score (logrank) test p = 8.14e-02 CYP51A1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.574 0.563 0.667 0.152 2.084 -0.860 0.390 Age 0.043 1.044 0.030 0.984 1.107 1.430 0.153 Gendermale 1.585 4.879 0.942 0.770 30.911 1.683 0.092 · RaceBlack 0.124 1.132 20126.201 0.000 Inf 0.000 1.000 RaceWhite 17.335 33776509.911 15856.327 0.000 Inf 0.001 0.999 Purity 5.849 347.059 3.331 0.507 237703.927 1.756 0.079 · Rsquare = 0.059 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.26e-01 Wald test p = 3.92e-01 Score (logrank) test p = 2.84e-01 CYP51A1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.299 1.349 0.445 0.564 3.226 0.673 0.501 Age 0.012 1.012 0.057 0.905 1.132 0.208 0.835 RaceBlack 15.178 3905999.678 6831.931 0.000 Inf 0.002 0.998 RaceWhite 16.367 12831962.213 6831.931 0.000 Inf 0.002 0.998 Purity 1.074 2.927 1.393 0.191 44.911 0.771 0.441 Rsquare = 0.008 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.75e-01 Wald test p = 7.88e-01 Score (logrank) test p = 7.2e-01 CYP51A1 in READ (n=166): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.951 2.588 0.659 0.712 9.411 1.443 0.149 Age 0.131 1.140 0.050 1.033 1.258 2.608 0.009 ** Gendermale -0.641 0.527 0.705 0.132 2.097 -0.909 0.363 RaceBlack 12.946 419111.202 10514.767 0.000 Inf 0.001 0.999 RaceWhite 11.729 124173.813 10514.767 0.000 Inf 0.001 0.999 Stage2 -1.702 0.182 1.252 0.016 2.123 -1.359 0.174 Stage3 -0.331 0.718 0.896 0.124 4.160 -0.370 0.712 Stage4 0.265 1.304 1.007 0.181 9.388 0.263 0.792 Purity 0.535 1.708 1.378 0.115 25.455 0.388 0.698 Rsquare = 0.23 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.85e-02 Wald test p = 2e-01 Score (logrank) test p = 4.01e-02 CYP51A1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.215 1.239 0.151 0.921 1.668 1.416 0.157 Age 0.022 1.023 0.008 1.006 1.039 2.676 0.007 ** Gendermale -0.012 0.988 0.223 0.638 1.528 -0.056 0.956 RaceBlack 0.053 1.055 1.093 0.124 8.986 0.049 0.961 RaceWhite -0.351 0.704 1.025 0.094 5.245 -0.343 0.732 Purity 0.885 2.423 0.579 0.779 7.533 1.529 0.126 Rsquare = 0.051 (max possible = 9.75e-01 ) Likelihood ratio test p = 5.92e-02 Wald test p = 8.2e-02 Score (logrank) test p = 8.13e-02 CYP51A1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.013 1.013 0.067 0.889 1.154 0.189 0.850 Age 0.018 1.019 0.005 1.008 1.029 3.551 0.000 *** Gendermale -0.050 0.951 0.157 0.699 1.295 -0.318 0.750 RaceWhite -1.289 0.276 0.402 0.125 0.606 -3.208 0.001 ** Stage2 0.275 1.316 0.218 0.858 2.019 1.259 0.208 Stage3 0.607 1.834 0.205 1.227 2.742 2.958 0.003 ** Stage4 1.351 3.860 0.352 1.937 7.689 3.841 0.000 *** Purity 1.016 2.762 0.340 1.417 5.382 2.985 0.003 ** Rsquare = 0.123 (max possible = 9.92e-01 ) Likelihood ratio test p = 2.2e-08 Wald test p = 1.19e-08 Score (logrank) test p = 1.42e-09 CYP51A1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.011 9.890000e-01 0.210 0.656 1.491 -0.053 0.958 Age 0.012 1.012000e+00 0.017 0.980 1.046 0.732 0.464 Gendermale 0.215 1.240000e+00 0.436 0.528 2.915 0.494 0.621 RaceWhite -1.267 2.820000e-01 0.625 0.083 0.958 -2.029 0.042 * Stage2 17.455 3.808569e+07 6199.669 0.000 Inf 0.003 0.998 Stage3 17.957 6.290479e+07 6199.669 0.000 Inf 0.003 0.998 Stage4 20.096 5.338876e+08 6199.669 0.000 Inf 0.003 0.997 Purity 0.275 1.316000e+00 0.946 0.206 8.400 0.291 0.771 Rsquare = 0.147 (max possible = 8.69e-01 ) Likelihood ratio test p = 6.13e-02 Wald test p = 5.5e-02 Score (logrank) test p = 4.57e-03 CYP51A1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.036 1.036 0.073 0.899 1.195 0.490 0.624 Age 0.021 1.021 0.006 1.010 1.032 3.668 0.000 *** Gendermale -0.058 0.943 0.172 0.673 1.322 -0.338 0.735 RaceWhite -1.067 0.344 0.600 0.106 1.115 -1.778 0.075 · Stage2 0.151 1.163 0.230 0.741 1.827 0.658 0.511 Stage3 0.550 1.734 0.210 1.148 2.618 2.618 0.009 ** Stage4 1.137 3.117 0.400 1.424 6.821 2.844 0.004 ** Purity 1.135 3.110 0.370 1.506 6.423 3.066 0.002 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 9.91e-07 Wald test p = 1.62e-06 Score (logrank) test p = 6.1e-07 CYP51A1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.017 1.018 0.094 0.846 1.223 0.185 0.853 Age 0.026 1.026 0.010 1.006 1.047 2.541 0.011 * Gendermale 0.118 1.125 0.209 0.747 1.693 0.564 0.573 RaceBlack 0.259 1.295 0.450 0.537 3.127 0.576 0.565 RaceWhite 0.088 1.092 0.246 0.674 1.770 0.358 0.721 Stage2 0.484 1.622 0.390 0.755 3.486 1.239 0.215 Stage3 0.916 2.499 0.364 1.225 5.100 2.517 0.012 * Stage4 1.319 3.738 0.504 1.392 10.043 2.615 0.009 ** Purity -0.564 0.569 0.383 0.268 1.206 -1.472 0.141 Rsquare = 0.069 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.36e-02 Wald test p = 1.88e-02 Score (logrank) test p = 1.53e-02 CYP51A1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -49.452 0.000000e+00 76820.501 0 Inf -0.001 0.999 Age -1.770 1.700000e-01 1664.029 0 Inf -0.001 0.999 RaceBlack 6.926 1.018191e+03 12781802.602 0 Inf 0.000 1.000 RaceWhite -76.424 0.000000e+00 12609255.659 0 Inf 0.000 1.000 Stage2 25.568 1.270212e+11 43614.278 0 Inf 0.001 1.000 Stage3 52.249 4.914407e+22 160933.646 0 Inf 0.000 1.000 Purity -27.442 0.000000e+00 232731.712 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.01e-03 CYP51A1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 1.444 4.236 0.693 1.090 16.469 2.084 0.037 * Age 0.146 1.157 0.027 1.098 1.220 5.445 0.000 *** Gendermale -0.227 0.797 0.656 0.220 2.883 -0.346 0.729 RaceBlack 17.533 41163894.039 9314.109 0.000 Inf 0.002 0.998 RaceWhite 17.382 35374765.573 9314.109 0.000 Inf 0.002 0.999 Stage2 0.798 2.222 1.172 0.223 22.105 0.681 0.496 Stage3 0.848 2.334 0.943 0.368 14.804 0.899 0.368 Stage4 2.274 9.720 1.075 1.183 79.879 2.116 0.034 * Purity 2.707 14.978 1.156 1.553 144.424 2.341 0.019 * Rsquare = 0.16 (max possible = 3.47e-01 ) Likelihood ratio test p = 2.55e-11 Wald test p = 3.31e-05 Score (logrank) test p = 3.71e-11 CYP51A1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.289 0.749 0.437 0.318 1.764 -0.661 0.508 Age 0.055 1.056 0.032 0.991 1.126 1.693 0.091 · Gendermale -0.200 0.819 0.739 0.192 3.486 -0.270 0.787 RaceBlack -16.784 0.000 9913.239 0.000 Inf -0.002 0.999 RaceWhite 0.434 1.543 1.099 0.179 13.293 0.395 0.693 Purity 0.476 1.609 1.093 0.189 13.696 0.435 0.663 Rsquare = 0.048 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.78e-01 Wald test p = 6.11e-01 Score (logrank) test p = 5.11e-01 CYP51A1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.296 1.344 0.191 0.925 1.953 1.553 0.120 Age 0.052 1.053 0.016 1.021 1.086 3.306 0.001 ** RaceBlack -0.399 0.671 0.796 0.141 3.197 -0.501 0.617 RaceWhite -0.561 0.570 0.748 0.132 2.473 -0.750 0.453 Purity 0.490 1.632 0.653 0.453 5.874 0.750 0.453 Rsquare = 0.047 (max possible = 7.81e-01 ) Likelihood ratio test p = 1.91e-02 Wald test p = 2.19e-02 Score (logrank) test p = 2.13e-02 CYP51A1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 -0.148 0.862 0.362 0.424 1.752 -0.410 0.682 Age 0.043 1.044 0.024 0.995 1.096 1.773 0.076 · RaceBlack 17.639 45775266.244 6474.898 0.000 Inf 0.003 0.998 RaceWhite 17.856 56844452.713 6474.898 0.000 Inf 0.003 0.998 Purity -0.919 0.399 1.058 0.050 3.171 -0.869 0.385 Rsquare = 0.122 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.4e-01 Wald test p = 3.5e-01 Score (logrank) test p = 2.59e-01 CYP51A1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `CYP51A1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif CYP51A1 0.760 2.137 0.278 1.240 3.683 2.735 0.006 ** Age 0.039 1.040 0.019 1.003 1.079 2.113 0.035 * Gendermale 0.425 1.529 0.471 0.608 3.848 0.902 0.367 Stage3 0.064 1.066 0.502 0.399 2.853 0.128 0.898 Stage4 3.916 50.180 1.218 4.607 546.615 3.214 0.001 ** Purity 1.720 5.583 1.312 0.427 73.034 1.311 0.190 Rsquare = 0.328 (max possible = 8.72e-01 ) Likelihood ratio test p = 2.97e-05 Wald test p = 6.41e-04 Score (logrank) test p = 1.6e-10 DHCR24 in ACC (n=79): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.108 0.897 0.136 0.688 1.171 -0.797 0.425 Age 0.004 1.004 0.014 0.978 1.031 0.282 0.778 Gendermale 0.315 1.370 0.423 0.598 3.139 0.744 0.457 RaceBlack 0.352 1.422 11999.913 0.000 Inf 0.000 1.000 RaceWhite 16.959 23190553.666 10210.869 0.000 Inf 0.002 0.999 Purity 3.646 38.313 2.591 0.239 6147.617 1.407 0.159 Rsquare = 0.075 (max possible = 9.38e-01 ) Likelihood ratio test p = 5.42e-01 Wald test p = 8.62e-01 Score (logrank) test p = 7.08e-01 DHCR24 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.078 1.082 0.061 0.960 1.219 1.289 0.197 Age 0.033 1.034 0.009 1.016 1.051 3.860 0.000 *** Gendermale -0.178 0.837 0.179 0.589 1.188 -0.997 0.319 RaceBlack 0.634 1.886 0.450 0.780 4.557 1.409 0.159 RaceWhite 0.063 1.065 0.358 0.528 2.147 0.176 0.860 Stage2 14.598 2187873.329 1816.493 0.000 Inf 0.008 0.994 Stage3 15.079 3539197.758 1816.493 0.000 Inf 0.008 0.993 Stage4 15.572 5793073.267 1816.493 0.000 Inf 0.009 0.993 Purity 0.172 1.188 0.343 0.607 2.325 0.502 0.616 Rsquare = 0.134 (max possible = 9.91e-01 ) Likelihood ratio test p = 9.73e-08 Wald test p = 7.19e-07 Score (logrank) test p = 2.33e-07 DHCR24 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.003 0.997 0.084 0.846 1.175 -0.039 0.969 Age 0.036 1.036 0.008 1.021 1.052 4.738 0.000 *** Gendermale 0.043 1.044 1.008 0.145 7.529 0.043 0.966 RaceBlack -0.002 0.998 0.621 0.295 3.375 -0.003 0.998 RaceWhite -0.223 0.800 0.596 0.249 2.572 -0.374 0.708 Stage2 0.407 1.503 0.305 0.827 2.730 1.337 0.181 Stage3 1.187 3.277 0.314 1.770 6.067 3.777 0.000 *** Stage4 2.514 12.356 0.389 5.768 26.466 6.469 0.000 *** Purity 0.515 1.673 0.422 0.732 3.826 1.220 0.223 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.48e-12 Wald test p = 6.83e-16 Score (logrank) test p = 8.58e-22 DHCR24 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.044 1.045000e+00 0.158 0.766 1.425 0.277 0.782 Age 0.010 1.010000e+00 0.018 0.976 1.046 0.595 0.552 RaceBlack -0.885 4.130000e-01 1.115 0.046 3.672 -0.794 0.427 RaceWhite -1.219 2.960000e-01 1.121 0.033 2.662 -1.087 0.277 Stage2 18.693 1.313505e+08 6464.965 0.000 Inf 0.003 0.998 Stage3 20.137 5.565472e+08 6464.965 0.000 Inf 0.003 0.998 Stage4 21.481 2.133581e+09 6464.965 0.000 Inf 0.003 0.997 Purity 0.792 2.208000e+00 0.965 0.333 14.639 0.820 0.412 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.06e-04 Wald test p = 6.64e-03 Score (logrank) test p = 4.42e-06 DHCR24 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.449 6.380000e-01 0.334 0.331 1.229 -1.343 0.179 Age 0.047 1.048000e+00 0.032 0.984 1.116 1.469 0.142 RaceBlack -3.574 2.800000e-02 1.905 0.001 1.175 -1.876 0.061 · RaceWhite -1.882 1.520000e-01 1.484 0.008 2.791 -1.268 0.205 Stage2 18.295 8.820652e+07 14447.825 0.000 Inf 0.001 0.999 Stage3 20.012 4.911582e+08 14447.825 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 2.428 1.133700e+01 2.314 0.121 1058.035 1.049 0.294 Rsquare = 0.389 (max possible = 6.68e-01 ) Likelihood ratio test p = 1.42e-04 Wald test p = 2.51e-01 Score (logrank) test p = 1.05e-14 DHCR24 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.001 0.999 0.181 0.701 1.426 -0.003 0.998 Age 0.049 1.050 0.012 1.026 1.074 4.091 0.000 *** Gendermale -15.362 0.000 3457.511 0.000 Inf -0.004 0.996 RaceBlack -0.434 0.648 1.174 0.065 6.474 -0.370 0.712 RaceWhite 0.246 1.280 1.033 0.169 9.692 0.239 0.811 Stage2 0.326 1.386 0.375 0.664 2.892 0.870 0.384 Stage3 0.862 2.368 0.402 1.078 5.203 2.147 0.032 * Stage4 2.151 8.597 0.592 2.696 27.416 3.636 0.000 *** Purity 0.307 1.360 0.625 0.400 4.626 0.492 0.623 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.04e-04 Wald test p = 2.01e-05 Score (logrank) test p = 4.05e-07 DHCR24 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.225 1.252 0.254 0.762 2.059 0.887 0.375 Age 0.053 1.055 0.021 1.012 1.100 2.501 0.012 * Gendermale 0.895 2.449 1.111 0.278 21.591 0.806 0.420 RaceBlack 16.655 17110321.918 6335.669 0.000 Inf 0.003 0.998 RaceWhite 15.951 8461485.028 6335.669 0.000 Inf 0.003 0.998 Stage2 0.697 2.007 1.074 0.245 16.462 0.649 0.516 Stage3 1.690 5.420 1.066 0.671 43.800 1.585 0.113 Stage4 2.145 8.544 1.169 0.864 84.492 1.835 0.067 · Purity 0.760 2.138 1.385 0.142 32.278 0.549 0.583 Rsquare = 0.11 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.2e-02 Wald test p = 6.46e-02 Score (logrank) test p = 2.16e-02 DHCR24 in CESC (n=306): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.177 1.193 0.160 0.873 1.631 1.108 0.268 Age 0.010 1.010 0.010 0.991 1.030 1.042 0.297 RaceBlack 1.015 2.760 1.068 0.340 22.399 0.950 0.342 RaceWhite 0.856 2.353 1.016 0.321 17.241 0.842 0.400 Purity 0.579 1.785 0.739 0.420 7.591 0.784 0.433 Rsquare = 0.019 (max possible = 8.91e-01 ) Likelihood ratio test p = 4.93e-01 Wald test p = 5.3e-01 Score (logrank) test p = 5.22e-01 DHCR24 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.030 1.030 0.306 0.565 1.878 0.097 0.923 Age 0.018 1.018 0.022 0.975 1.063 0.816 0.414 Gendermale 0.246 1.279 0.564 0.423 3.866 0.436 0.663 RaceBlack -0.414 0.661 1.636 0.027 16.302 -0.253 0.800 RaceWhite -1.079 0.340 0.895 0.059 1.965 -1.205 0.228 Stage2 0.663 1.941 0.671 0.521 7.227 0.988 0.323 Stage3 -15.344 0.000 6949.181 0.000 Inf -0.002 0.998 Stage4 0.845 2.329 0.706 0.583 9.297 1.197 0.231 Purity 2.124 8.362 1.810 0.241 290.641 1.173 0.241 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.79e-01 Wald test p = 6.53e-01 Score (logrank) test p = 4.86e-01 DHCR24 in COAD (n=458): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.063 0.939 0.185 0.653 1.349 -0.342 0.732 Age 0.024 1.024 0.011 1.001 1.047 2.070 0.038 * Gendermale 0.205 1.227 0.270 0.723 2.085 0.758 0.449 RaceBlack -0.442 0.643 0.831 0.126 3.274 -0.532 0.595 RaceWhite -0.479 0.620 0.782 0.134 2.868 -0.612 0.540 Stage2 0.202 1.224 0.563 0.406 3.688 0.359 0.719 Stage3 0.806 2.239 0.549 0.763 6.565 1.468 0.142 Stage4 1.884 6.581 0.552 2.230 19.423 3.412 0.001 ** Purity -0.198 0.820 0.600 0.253 2.659 -0.330 0.741 Rsquare = 0.11 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.44e-04 Wald test p = 1.47e-04 Score (logrank) test p = 2.14e-05 DHCR24 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 1.129 3.094 0.663 0.844 11.336 1.704 0.088 · Age -0.008 0.992 0.045 0.908 1.084 -0.174 0.862 Gendermale 1.051 2.861 1.148 0.301 27.156 0.915 0.360 RaceBlack 1.234 3.435 1.938 0.077 153.153 0.637 0.524 RaceWhite -2.999 0.050 1.462 0.003 0.875 -2.051 0.040 * Purity -1.346 0.260 2.257 0.003 21.707 -0.596 0.551 Rsquare = 0.2 (max possible = 5.58e-01 ) Likelihood ratio test p = 1.65e-01 Wald test p = 3.42e-01 Score (logrank) test p = 1.41e-01 DHCR24 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.013 1.013 0.179 0.714 1.439 0.075 0.941 Age 0.010 1.010 0.014 0.982 1.038 0.680 0.496 Gendermale 0.487 1.627 0.543 0.562 4.712 0.897 0.370 RaceBlack 0.320 1.377 1.090 0.162 11.674 0.294 0.769 RaceWhite -0.073 0.929 0.453 0.382 2.258 -0.162 0.871 Stage2 0.700 2.014 0.655 0.558 7.277 1.069 0.285 Stage3 1.453 4.274 0.671 1.148 15.911 2.166 0.030 * Stage4 2.861 17.485 0.775 3.831 79.800 3.694 0.000 *** Purity 0.211 1.235 0.770 0.273 5.592 0.274 0.784 Rsquare = 0.141 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.15e-02 Wald test p = 5.33e-03 Score (logrank) test p = 4.43e-04 DHCR24 in GBM (n=153): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.038 0.963 0.079 0.824 1.124 -0.480 0.631 Age 0.030 1.030 0.008 1.014 1.047 3.604 0.000 *** Gendermale -0.081 0.922 0.215 0.605 1.406 -0.377 0.706 RaceBlack 0.592 1.807 0.738 0.426 7.674 0.802 0.422 RaceWhite -0.194 0.824 0.621 0.244 2.782 -0.312 0.755 Purity -1.106 0.331 0.536 0.116 0.946 -2.063 0.039 * Rsquare = 0.13 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.38e-03 Wald test p = 6.63e-03 Score (logrank) test p = 5.74e-03 DHCR24 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.095 1.100 0.098 0.907 1.333 0.966 0.334 Age 0.022 1.022 0.008 1.007 1.038 2.878 0.004 ** Gendermale -0.245 0.783 0.172 0.559 1.096 -1.428 0.153 RaceBlack 0.093 1.097 0.560 0.366 3.290 0.165 0.869 RaceWhite -0.295 0.745 0.513 0.272 2.037 -0.574 0.566 Stage2 0.621 1.861 0.544 0.641 5.402 1.143 0.253 Stage3 0.873 2.394 0.537 0.836 6.857 1.626 0.104 Stage4 1.270 3.561 0.510 1.311 9.675 2.490 0.013 * Purity -0.032 0.968 0.366 0.472 1.986 -0.088 0.930 Rsquare = 0.071 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.64e-04 Wald test p = 1.01e-03 Score (logrank) test p = 7.22e-04 DHCR24 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.081 1.084000e+00 0.249 0.665 1.768 0.325 0.745 Age 0.010 1.010000e+00 0.025 0.961 1.061 0.390 0.697 Gendermale -0.145 8.650000e-01 0.537 0.302 2.479 -0.270 0.787 RaceBlack 18.851 1.537308e+08 12028.092 0.000 Inf 0.002 0.999 RaceWhite 18.069 7.035978e+07 12028.092 0.000 Inf 0.002 0.999 Stage2 17.464 3.840878e+07 5302.015 0.000 Inf 0.003 0.997 Stage3 16.661 1.720980e+07 5302.015 0.000 Inf 0.003 0.997 Stage4 17.498 3.975417e+07 5302.015 0.000 Inf 0.003 0.997 Purity -1.572 2.080000e-01 1.071 0.025 1.695 -1.468 0.142 Rsquare = 0.089 (max possible = 9.17e-01 ) Likelihood ratio test p = 7.37e-01 Wald test p = 9.51e-01 Score (logrank) test p = 8.63e-01 DHCR24 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.099 1.104 0.111 0.889 1.371 0.896 0.370 Age 0.027 1.027 0.008 1.010 1.044 3.192 0.001 ** Gendermale -0.282 0.754 0.183 0.527 1.079 -1.542 0.123 RaceBlack -0.064 0.938 0.566 0.309 2.847 -0.112 0.911 RaceWhite -0.450 0.638 0.516 0.232 1.753 -0.871 0.383 Stage2 0.362 1.437 0.554 0.485 4.253 0.654 0.513 Stage3 0.740 2.095 0.541 0.726 6.048 1.368 0.171 Stage4 1.159 3.187 0.512 1.169 8.691 2.264 0.024 * Purity 0.232 1.261 0.404 0.571 2.782 0.574 0.566 Rsquare = 0.087 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.48e-04 Wald test p = 7.39e-04 Score (logrank) test p = 5.46e-04 DHCR24 in KICH (n=66): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p DHCR24 0.285 1.329000e+00 0.285 0.761 2.323000e+00 0.999 0.318 Age 0.075 1.078000e+00 0.029 1.019 1.141000e+00 2.603 0.009 Gendermale -0.759 4.680000e-01 0.727 0.113 1.946000e+00 -1.044 0.297 RaceBlack -16.577 0.000000e+00 6338.450 0.000 Inf -0.003 0.998 RaceWhite -1.314 2.690000e-01 1.159 0.028 2.607000e+00 -1.133 0.257 Stage2 15.924 8.233866e+06 0.846 1568014.938 4.323718e+07 18.819 0.000 Stage3 16.943 2.281405e+07 0.777 4973537.090 1.046500e+08 21.800 0.000 Stage4 19.217 2.218356e+08 0.894 38475110.238 1.279036e+09 21.499 0.000 Purity 0.597 1.816000e+00 3.702 0.001 2.570926e+03 0.161 0.872 signif DHCR24 Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.352 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.23e-03 Wald test p = 1.25e-274 Score (logrank) test p = 9.16e-09 DHCR24 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.072 0.931 0.092 0.777 1.115 -0.777 0.437 Age 0.036 1.036 0.008 1.019 1.054 4.209 0.000 *** Gendermale -0.088 0.915 0.184 0.638 1.313 -0.480 0.631 RaceBlack 0.202 1.224 1.056 0.154 9.693 0.191 0.848 RaceWhite 0.142 1.152 1.014 0.158 8.411 0.140 0.889 Stage2 0.207 1.230 0.344 0.627 2.416 0.602 0.547 Stage3 0.800 2.227 0.230 1.418 3.497 3.477 0.001 ** Stage4 1.759 5.806 0.216 3.804 8.861 8.154 0.000 *** Purity -0.001 0.999 0.366 0.487 2.049 -0.002 0.998 Rsquare = 0.175 (max possible = 9.65e-01 ) Likelihood ratio test p = 7.87e-15 Wald test p = 8.22e-15 Score (logrank) test p = 4.27e-18 DHCR24 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.083 0.921 0.183 0.644 1.317 -0.453 0.651 Age 0.006 1.006 0.016 0.976 1.038 0.400 0.689 Gendermale -0.490 0.612 0.381 0.290 1.293 -1.286 0.198 RaceBlack -2.084 0.124 1.203 0.012 1.314 -1.733 0.083 · RaceWhite -2.134 0.118 1.186 0.012 1.210 -1.800 0.072 · Stage2 -0.391 0.676 1.056 0.085 5.353 -0.371 0.711 Stage3 1.656 5.237 0.427 2.267 12.098 3.876 0.000 *** Stage4 2.752 15.671 0.517 5.684 43.209 5.318 0.000 *** Purity -0.285 0.752 0.748 0.174 3.261 -0.380 0.704 Rsquare = 0.164 (max possible = 7.58e-01 ) Likelihood ratio test p = 1.07e-05 Wald test p = 3.36e-06 Score (logrank) test p = 5.76e-10 DHCR24 in LAML (n=173): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.196 1.216 0.085 1.029 1.437 2.293 0.022 * Age 0.043 1.044 0.009 1.027 1.063 4.942 0.000 *** Gendermale -0.113 0.893 0.212 0.590 1.352 -0.536 0.592 RaceBlack -0.558 0.572 1.109 0.065 5.034 -0.503 0.615 RaceWhite -0.918 0.399 1.023 0.054 2.966 -0.897 0.370 Rsquare = 0.185 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.16e-05 Wald test p = 9.22e-05 Score (logrank) test p = 5.45e-05 DHCR24 in LGG (n=516): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.056 1.058 0.095 0.879 1.273 0.594 0.553 Age 0.062 1.064 0.008 1.048 1.080 8.035 0.000 *** Gendermale 0.112 1.118 0.198 0.758 1.649 0.564 0.573 RaceBlack 15.432 5034952.250 1978.024 0.000 Inf 0.008 0.994 RaceWhite 15.434 5045977.035 1978.024 0.000 Inf 0.008 0.994 Purity -0.910 0.403 0.412 0.180 0.903 -2.209 0.027 * Rsquare = 0.137 (max possible = 9.07e-01 ) Likelihood ratio test p = 9.71e-13 Wald test p = 1.11e-12 Score (logrank) test p = 9.01e-14 DHCR24 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.012 1.012 0.129 0.786 1.304 0.095 0.924 Age 0.011 1.011 0.008 0.995 1.027 1.324 0.185 Gendermale -0.140 0.869 0.225 0.559 1.352 -0.622 0.534 RaceBlack 0.889 2.433 0.490 0.931 6.358 1.815 0.070 · RaceWhite 0.005 1.005 0.238 0.630 1.602 0.020 0.984 Stage2 0.314 1.369 0.261 0.821 2.285 1.203 0.229 Stage3 0.948 2.580 0.235 1.627 4.092 4.028 0.000 *** Stage4 1.592 4.913 0.618 1.462 16.512 2.574 0.010 * Purity 0.569 1.767 0.467 0.708 4.411 1.219 0.223 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.2e-03 Wald test p = 7.18e-04 Score (logrank) test p = 2.71e-04 DHCR24 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.067 0.935 0.095 0.776 1.126 -0.707 0.479 Age 0.008 1.008 0.009 0.990 1.026 0.847 0.397 Gendermale 0.013 1.013 0.169 0.727 1.412 0.078 0.938 RaceBlack 16.007 8950840.034 1881.482 0.000 Inf 0.009 0.993 RaceWhite 16.195 10799553.543 1881.482 0.000 Inf 0.009 0.993 Stage2 0.849 2.336 0.202 1.572 3.472 4.196 0.000 *** Stage3 1.011 2.748 0.218 1.793 4.213 4.638 0.000 *** Stage4 1.013 2.754 0.334 1.430 5.301 3.031 0.002 ** Purity 0.620 1.859 0.346 0.944 3.661 1.794 0.073 · Rsquare = 0.098 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.94e-06 Wald test p = 2.53e-05 Score (logrank) test p = 2.89e-06 DHCR24 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.075 1.078 0.093 0.898 1.294 0.804 0.422 Age 0.016 1.016 0.009 0.997 1.034 1.663 0.096 · Gendermale 0.429 1.535 0.193 1.051 2.243 2.215 0.027 * RaceBlack -0.010 0.990 0.609 0.300 3.268 -0.017 0.987 RaceWhite -0.538 0.584 0.566 0.192 1.771 -0.950 0.342 Stage2 0.216 1.241 0.187 0.861 1.789 1.159 0.247 Stage3 0.595 1.812 0.214 1.190 2.759 2.772 0.006 ** Stage4 0.750 2.117 0.798 0.443 10.120 0.940 0.347 Purity -0.295 0.744 0.370 0.360 1.538 -0.797 0.425 Rsquare = 0.052 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.94e-02 Wald test p = 1.4e-02 Score (logrank) test p = 1.21e-02 DHCR24 in MESO (n=87): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.006 0.994 0.129 0.773 1.279 -0.044 0.965 Age 0.020 1.020 0.016 0.989 1.052 1.275 0.202 Gendermale -0.174 0.840 0.335 0.436 1.619 -0.521 0.602 RaceBlack 0.147 1.158 1.551 0.055 24.192 0.095 0.925 RaceWhite -0.500 0.606 1.058 0.076 4.820 -0.473 0.636 Stage2 -0.233 0.793 0.471 0.315 1.995 -0.494 0.622 Stage3 -0.103 0.902 0.420 0.396 2.056 -0.245 0.807 Stage4 -0.152 0.859 0.477 0.337 2.189 -0.318 0.751 Purity -0.754 0.471 0.553 0.159 1.391 -1.363 0.173 Rsquare = 0.06 (max possible = 9.98e-01 ) Likelihood ratio test p = 8.11e-01 Wald test p = 7.9e-01 Score (logrank) test p = 7.82e-01 DHCR24 in OV (n=303): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.056 1.058 0.080 0.905 1.237 0.704 0.481 Age 0.038 1.038 0.008 1.021 1.056 4.445 0.000 *** RaceBlack -0.028 0.972 0.578 0.313 3.018 -0.048 0.961 RaceWhite -0.150 0.860 0.515 0.313 2.363 -0.292 0.770 Purity -0.513 0.599 0.672 0.161 2.233 -0.764 0.445 Rsquare = 0.083 (max possible = 9.97e-01 ) Likelihood ratio test p = 9.32e-04 Wald test p = 9.09e-04 Score (logrank) test p = 7.41e-04 DHCR24 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.187 1.206 0.133 0.929 1.565 1.408 0.159 Age 0.020 1.020 0.011 0.999 1.042 1.874 0.061 · Gendermale -0.218 0.804 0.217 0.525 1.229 -1.007 0.314 RaceBlack -0.047 0.954 0.738 0.225 4.051 -0.064 0.949 RaceWhite 0.361 1.435 0.474 0.567 3.632 0.762 0.446 Stage2 0.429 1.536 0.449 0.638 3.701 0.957 0.339 Stage3 -0.240 0.787 1.089 0.093 6.652 -0.220 0.826 Stage4 -0.142 0.868 0.864 0.160 4.722 -0.164 0.870 Purity -0.504 0.604 0.427 0.262 1.395 -1.180 0.238 Rsquare = 0.1 (max possible = 9.91e-01 ) Likelihood ratio test p = 4.33e-02 Wald test p = 8.35e-02 Score (logrank) test p = 7.73e-02 DHCR24 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.272 0.762 0.288 0.433 1.340 -0.944 0.345 Age 0.047 1.048 0.030 0.988 1.113 1.554 0.120 Gendermale 1.623 5.067 0.943 0.798 32.189 1.720 0.085 · RaceBlack -0.172 0.842 19718.643 0.000 Inf 0.000 1.000 RaceWhite 17.139 27761173.167 15611.944 0.000 Inf 0.001 0.999 Purity 6.351 572.847 3.645 0.453 725132.267 1.742 0.081 · Rsquare = 0.06 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.18e-01 Wald test p = 4.48e-01 Score (logrank) test p = 3.02e-01 DHCR24 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.111 1.117 0.296 0.625 1.996 0.374 0.708 Age 0.010 1.010 0.057 0.903 1.129 0.171 0.864 RaceBlack 15.052 3442692.133 6750.988 0.000 Inf 0.002 0.998 RaceWhite 16.287 11839401.453 6750.988 0.000 Inf 0.002 0.998 Purity 1.153 3.166 1.400 0.204 49.238 0.823 0.410 Rsquare = 0.007 (max possible = 1.83e-01 ) Likelihood ratio test p = 7.22e-01 Wald test p = 8.45e-01 Score (logrank) test p = 7.9e-01 DHCR24 in READ (n=166): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.203 1.225 0.556 0.412 3.642 0.366 0.714 Age 0.114 1.121 0.046 1.024 1.227 2.481 0.013 * Gendermale -0.351 0.704 0.689 0.182 2.717 -0.509 0.610 RaceBlack 13.243 564056.319 10298.513 0.000 Inf 0.001 0.999 RaceWhite 12.130 185403.944 10298.513 0.000 Inf 0.001 0.999 Stage2 -1.822 0.162 1.260 0.014 1.912 -1.446 0.148 Stage3 -0.372 0.689 0.947 0.108 4.412 -0.393 0.695 Stage4 -0.078 0.925 0.967 0.139 6.163 -0.080 0.936 Purity 0.450 1.568 1.610 0.067 36.782 0.280 0.780 Rsquare = 0.211 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.54e-02 Wald test p = 2.67e-01 Score (logrank) test p = 4.73e-02 DHCR24 in SARC (n=260): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.113 1.119 0.081 0.956 1.311 1.397 0.162 Age 0.021 1.022 0.008 1.005 1.038 2.560 0.010 * Gendermale 0.021 1.021 0.224 0.659 1.583 0.093 0.926 RaceBlack -0.323 0.724 1.095 0.085 6.194 -0.295 0.768 RaceWhite -0.610 0.543 1.029 0.072 4.081 -0.593 0.553 Purity 0.896 2.449 0.574 0.795 7.543 1.560 0.119 Rsquare = 0.051 (max possible = 9.75e-01 ) Likelihood ratio test p = 5.93e-02 Wald test p = 8.57e-02 Score (logrank) test p = 8.55e-02 DHCR24 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.080 1.083 0.046 0.989 1.186 1.731 0.084 · Age 0.018 1.018 0.005 1.008 1.029 3.511 0.000 *** Gendermale -0.049 0.952 0.157 0.699 1.297 -0.310 0.756 RaceWhite -1.295 0.274 0.402 0.125 0.602 -3.225 0.001 ** Stage2 0.265 1.303 0.219 0.849 2.002 1.211 0.226 Stage3 0.640 1.897 0.205 1.270 2.833 3.125 0.002 ** Stage4 1.372 3.943 0.353 1.975 7.872 3.889 0.000 *** Purity 1.084 2.957 0.343 1.510 5.793 3.160 0.002 ** Rsquare = 0.13 (max possible = 9.92e-01 ) Likelihood ratio test p = 5.54e-09 Wald test p = 4.21e-09 Score (logrank) test p = 4.08e-10 DHCR24 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.260 1.297000e+00 0.174 0.922 1.824 1.495 0.135 Age 0.014 1.014000e+00 0.016 0.983 1.046 0.886 0.376 Gendermale 0.300 1.350000e+00 0.439 0.571 3.190 0.684 0.494 RaceWhite -1.128 3.240000e-01 0.634 0.093 1.122 -1.778 0.075 · Stage2 17.531 4.107961e+07 6266.023 0.000 Inf 0.003 0.998 Stage3 18.061 6.979422e+07 6266.023 0.000 Inf 0.003 0.998 Stage4 20.611 8.937135e+08 6266.023 0.000 Inf 0.003 0.997 Purity 0.373 1.453000e+00 0.925 0.237 8.903 0.404 0.686 Rsquare = 0.17 (max possible = 8.69e-01 ) Likelihood ratio test p = 2.51e-02 Wald test p = 3.47e-02 Score (logrank) test p = 2.13e-03 DHCR24 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.067 1.069 0.049 0.971 1.177 1.355 0.175 Age 0.020 1.021 0.006 1.009 1.032 3.631 0.000 *** Gendermale -0.062 0.940 0.172 0.671 1.318 -0.358 0.721 RaceWhite -1.105 0.331 0.601 0.102 1.076 -1.838 0.066 · Stage2 0.140 1.150 0.231 0.731 1.809 0.604 0.546 Stage3 0.587 1.799 0.210 1.192 2.714 2.799 0.005 ** Stage4 1.147 3.150 0.401 1.436 6.908 2.863 0.004 ** Purity 1.196 3.305 0.373 1.590 6.872 3.201 0.001 ** Rsquare = 0.139 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.82e-07 Wald test p = 9.32e-07 Score (logrank) test p = 3.24e-07 DHCR24 in STAD (n=415): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.134 0.874 0.084 0.741 1.031 -1.592 0.111 Age 0.028 1.028 0.010 1.008 1.049 2.689 0.007 ** Gendermale 0.153 1.165 0.208 0.774 1.753 0.733 0.464 RaceBlack 0.338 1.403 0.450 0.581 3.389 0.752 0.452 RaceWhite 0.178 1.194 0.251 0.731 1.952 0.708 0.479 Stage2 0.539 1.715 0.392 0.795 3.701 1.375 0.169 Stage3 0.988 2.685 0.367 1.308 5.515 2.690 0.007 ** Stage4 1.392 4.024 0.507 1.491 10.859 2.749 0.006 ** Purity -0.482 0.617 0.381 0.293 1.302 -1.266 0.205 Rsquare = 0.077 (max possible = 9.79e-01 ) Likelihood ratio test p = 5.68e-03 Wald test p = 7.92e-03 Score (logrank) test p = 6.16e-03 DHCR24 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -1.334 2.630000e-01 12492.784 0 Inf 0.000 1.000 Age -1.719 1.790000e-01 1660.242 0 Inf -0.001 0.999 RaceBlack 8.497 4.902335e+03 18885028.116 0 Inf 0.000 1.000 RaceWhite -35.481 0.000000e+00 19646737.648 0 Inf 0.000 1.000 Stage2 -2.560 7.700000e-02 40483.683 0 Inf 0.000 1.000 Stage3 17.958 6.293014e+07 133266.284 0 Inf 0.000 1.000 Purity 21.145 1.524695e+09 197347.054 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.3e-03 DHCR24 in THCA (n=509): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.099 1.104 0.314 0.596 2.045 0.315 0.753 Age 0.147 1.158 0.028 1.096 1.224 5.213 0.000 *** Gendermale -0.013 0.987 0.640 0.282 3.462 -0.020 0.984 RaceBlack 17.771 52233224.573 9283.385 0.000 Inf 0.002 0.998 RaceWhite 17.597 43865581.805 9283.385 0.000 Inf 0.002 0.998 Stage2 -0.118 0.888 1.119 0.099 7.968 -0.106 0.916 Stage3 0.246 1.279 0.853 0.240 6.811 0.288 0.773 Stage4 1.750 5.754 0.994 0.821 40.329 1.761 0.078 · Purity 2.207 9.091 1.089 1.075 76.856 2.027 0.043 * Rsquare = 0.149 (max possible = 3.47e-01 ) Likelihood ratio test p = 2.43e-10 Wald test p = 3.68e-04 Score (logrank) test p = 1.23e-10 DHCR24 in THYM (n=120): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.395 0.674 0.365 0.329 1.378 -1.082 0.279 Age 0.039 1.040 0.032 0.976 1.108 1.209 0.227 Gendermale -0.192 0.825 0.731 0.197 3.454 -0.263 0.792 RaceBlack -16.682 0.000 9605.108 0.000 Inf -0.002 0.999 RaceWhite 0.308 1.361 1.105 0.156 11.864 0.279 0.780 Purity 0.137 1.147 1.112 0.130 10.139 0.123 0.902 Rsquare = 0.054 (max possible = 4.51e-01 ) Likelihood ratio test p = 3.88e-01 Wald test p = 4.74e-01 Score (logrank) test p = 3.72e-01 DHCR24 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.281 1.325 0.156 0.976 1.799 1.803 0.071 · Age 0.044 1.045 0.017 1.012 1.079 2.668 0.008 ** RaceBlack -0.419 0.658 0.797 0.138 3.136 -0.526 0.599 RaceWhite -0.451 0.637 0.748 0.147 2.759 -0.603 0.546 Purity 0.218 1.244 0.665 0.338 4.575 0.328 0.743 Rsquare = 0.049 (max possible = 7.81e-01 ) Likelihood ratio test p = 1.37e-02 Wald test p = 2.35e-02 Score (logrank) test p = 1.9e-02 DHCR24 in UCS (n=57): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 -0.114 0.893 0.272 0.524 1.522 -0.417 0.676 Age 0.047 1.048 0.026 0.997 1.103 1.838 0.066 · RaceBlack 17.436 37354127.703 6473.588 0.000 Inf 0.003 0.998 RaceWhite 17.691 48220648.644 6473.588 0.000 Inf 0.003 0.998 Purity -0.719 0.487 1.117 0.055 4.349 -0.644 0.520 Rsquare = 0.122 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.39e-01 Wald test p = 3.49e-01 Score (logrank) test p = 2.56e-01 DHCR24 in UVM (n=80): Model: Surv(OS, EVENT) ~ `DHCR24` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR24 0.454 1.574 0.232 1.000 2.478 1.958 0.050 · Age 0.034 1.035 0.018 0.998 1.073 1.859 0.063 · Gendermale 0.330 1.390 0.492 0.530 3.650 0.669 0.503 Stage3 -0.155 0.856 0.544 0.295 2.488 -0.285 0.775 Stage4 3.769 43.338 1.211 4.040 464.928 3.113 0.002 ** Purity 2.207 9.088 1.308 0.701 117.897 1.688 0.091 · Rsquare = 0.292 (max possible = 8.72e-01 ) Likelihood ratio test p = 1.75e-04 Wald test p = 1.52e-03 Score (logrank) test p = 6.39e-10 DHCR7 in ACC (n=79): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.203 1.225 0.155 0.904 1.660 1.311 0.190 Age 0.010 1.010 0.015 0.982 1.039 0.675 0.500 Gendermale 0.473 1.604 0.422 0.702 3.668 1.121 0.262 RaceBlack -0.645 0.525 12191.016 0.000 Inf 0.000 1.000 RaceWhite 16.335 12416846.823 10401.423 0.000 Inf 0.002 0.999 Purity 2.186 8.897 2.277 0.102 772.222 0.960 0.337 Rsquare = 0.093 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.93e-01 Wald test p = 6.98e-01 Score (logrank) test p = 5.35e-01 DHCR7 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.156 1.169 0.094 0.973 1.404 1.664 0.096 · Age 0.031 1.031 0.009 1.014 1.049 3.602 0.000 *** Gendermale -0.193 0.825 0.179 0.580 1.172 -1.075 0.282 RaceBlack 0.636 1.889 0.447 0.786 4.537 1.422 0.155 RaceWhite 0.125 1.133 0.353 0.567 2.265 0.355 0.723 Stage2 14.453 1891806.024 1882.767 0.000 Inf 0.008 0.994 Stage3 14.929 3044396.454 1882.767 0.000 Inf 0.008 0.994 Stage4 15.401 4880386.693 1882.767 0.000 Inf 0.008 0.993 Purity 0.025 1.025 0.348 0.519 2.027 0.072 0.943 Rsquare = 0.137 (max possible = 9.91e-01 ) Likelihood ratio test p = 6.15e-08 Wald test p = 3.39e-07 Score (logrank) test p = 9.91e-08 DHCR7 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.047 1.048 0.095 0.869 1.263 0.490 0.624 Age 0.037 1.037 0.008 1.022 1.053 4.740 0.000 *** Gendermale 0.029 1.030 1.007 0.143 7.419 0.029 0.977 RaceBlack -0.029 0.971 0.622 0.287 3.285 -0.047 0.962 RaceWhite -0.251 0.778 0.599 0.241 2.514 -0.420 0.674 Stage2 0.406 1.501 0.304 0.827 2.722 1.336 0.182 Stage3 1.179 3.250 0.313 1.758 6.008 3.760 0.000 *** Stage4 2.495 12.119 0.391 5.637 26.053 6.389 0.000 *** Purity 0.477 1.611 0.429 0.695 3.733 1.112 0.266 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.23e-12 Wald test p = 5.75e-16 Score (logrank) test p = 6.91e-22 DHCR7 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.157 8.550000e-01 0.300 0.475 1.538 -0.524 0.600 Age 0.009 1.009000e+00 0.018 0.975 1.045 0.514 0.607 RaceBlack -0.882 4.140000e-01 1.107 0.047 3.623 -0.797 0.425 RaceWhite -1.202 3.010000e-01 1.113 0.034 2.664 -1.080 0.280 Stage2 18.671 1.285057e+08 6489.429 0.000 Inf 0.003 0.998 Stage3 20.093 5.323729e+08 6489.429 0.000 Inf 0.003 0.998 Stage4 21.446 2.060772e+09 6489.429 0.000 Inf 0.003 0.997 Purity 0.835 2.306000e+00 0.958 0.352 15.086 0.872 0.383 Rsquare = 0.158 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.68e-04 Wald test p = 7.04e-03 Score (logrank) test p = 4.25e-06 DHCR7 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.692 5.010000e-01 0.492 0.191 1.314 -1.406 0.160 Age 0.031 1.031000e+00 0.029 0.974 1.092 1.055 0.291 RaceBlack -2.638 7.100000e-02 1.837 0.002 2.618 -1.436 0.151 RaceWhite -1.513 2.200000e-01 1.450 0.013 3.776 -1.044 0.297 Stage2 16.969 2.342070e+07 10801.894 0.000 Inf 0.002 0.999 Stage3 18.977 1.743769e+08 10801.894 0.000 Inf 0.002 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 2.494 1.210400e+01 2.391 0.112 1313.235 1.043 0.297 Rsquare = 0.392 (max possible = 6.68e-01 ) Likelihood ratio test p = 1.27e-04 Wald test p = 1.87e-01 Score (logrank) test p = 1.62e-15 DHCR7 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.053 0.949 0.153 0.703 1.279 -0.346 0.729 Age 0.048 1.049 0.012 1.024 1.074 3.965 0.000 *** Gendermale -15.327 0.000 3457.311 0.000 Inf -0.004 0.996 RaceBlack -0.407 0.666 1.177 0.066 6.691 -0.346 0.730 RaceWhite 0.289 1.335 1.039 0.174 10.238 0.278 0.781 Stage2 0.324 1.383 0.374 0.664 2.879 0.867 0.386 Stage3 0.863 2.370 0.394 1.096 5.126 2.193 0.028 * Stage4 2.159 8.660 0.592 2.713 27.648 3.645 0.000 *** Purity 0.353 1.423 0.625 0.418 4.846 0.564 0.573 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 9.92e-05 Wald test p = 2e-05 Score (logrank) test p = 4.05e-07 DHCR7 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.119 1.127 0.185 0.783 1.620 0.643 0.521 Age 0.049 1.050 0.021 1.008 1.094 2.366 0.018 * Gendermale 0.957 2.605 1.109 0.296 22.895 0.863 0.388 RaceBlack 16.514 14856393.425 6377.299 0.000 Inf 0.003 0.998 RaceWhite 15.895 7996527.834 6377.299 0.000 Inf 0.002 0.998 Stage2 0.649 1.913 1.075 0.233 15.733 0.604 0.546 Stage3 1.503 4.493 1.071 0.551 36.664 1.403 0.161 Stage4 2.018 7.527 1.176 0.751 75.410 1.717 0.086 · Purity 1.082 2.950 1.355 0.207 41.972 0.799 0.425 Rsquare = 0.107 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.69e-02 Wald test p = 6.33e-02 Score (logrank) test p = 2.02e-02 DHCR7 in CESC (n=306): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.205 1.228 0.154 0.908 1.661 1.334 0.182 Age 0.009 1.009 0.010 0.990 1.029 0.939 0.348 RaceBlack 0.986 2.681 1.069 0.330 21.783 0.923 0.356 RaceWhite 0.795 2.215 1.015 0.303 16.203 0.783 0.433 Purity 0.383 1.466 0.748 0.339 6.346 0.512 0.609 Rsquare = 0.021 (max possible = 8.91e-01 ) Likelihood ratio test p = 4.29e-01 Wald test p = 4.46e-01 Score (logrank) test p = 4.36e-01 DHCR7 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.329 1.389 0.385 0.653 2.954 0.853 0.393 Age 0.024 1.024 0.023 0.980 1.071 1.055 0.291 Gendermale 0.151 1.163 0.576 0.376 3.597 0.262 0.794 RaceBlack -0.343 0.710 1.463 0.040 12.491 -0.234 0.815 RaceWhite -1.048 0.351 0.873 0.063 1.942 -1.200 0.230 Stage2 0.763 2.145 0.663 0.585 7.867 1.151 0.250 Stage3 -14.819 0.000 6986.947 0.000 Inf -0.002 0.998 Stage4 0.674 1.962 0.705 0.492 7.813 0.956 0.339 Purity 2.339 10.371 1.635 0.421 255.619 1.431 0.153 Rsquare = 0.226 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.15e-01 Wald test p = 5.75e-01 Score (logrank) test p = 4.13e-01 DHCR7 in COAD (n=458): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.181 0.834 0.211 0.551 1.261 -0.860 0.390 Age 0.024 1.024 0.012 1.001 1.048 2.072 0.038 * Gendermale 0.191 1.210 0.270 0.712 2.056 0.706 0.480 RaceBlack -0.541 0.582 0.840 0.112 3.017 -0.645 0.519 RaceWhite -0.563 0.569 0.787 0.122 2.662 -0.716 0.474 Stage2 0.228 1.257 0.563 0.417 3.786 0.406 0.685 Stage3 0.852 2.344 0.551 0.795 6.906 1.545 0.122 Stage4 1.960 7.101 0.559 2.373 21.251 3.505 0.000 *** Purity -0.190 0.827 0.595 0.257 2.654 -0.320 0.749 Rsquare = 0.112 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.49e-04 Wald test p = 1.18e-04 Score (logrank) test p = 1.71e-05 DHCR7 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.946 2.575 0.670 0.692 9.578 1.411 0.158 Age -0.005 0.995 0.044 0.913 1.083 -0.120 0.904 Gendermale 1.125 3.079 1.178 0.306 30.967 0.955 0.340 RaceBlack 1.291 3.637 1.866 0.094 140.882 0.692 0.489 RaceWhite -2.584 0.076 1.378 0.005 1.124 -1.875 0.061 · Purity -1.710 0.181 2.164 0.003 12.572 -0.790 0.429 Rsquare = 0.174 (max possible = 5.58e-01 ) Likelihood ratio test p = 2.49e-01 Wald test p = 4.66e-01 Score (logrank) test p = 2.32e-01 DHCR7 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.091 1.095 0.137 0.837 1.432 0.664 0.507 Age 0.011 1.011 0.014 0.983 1.039 0.759 0.448 Gendermale 0.559 1.748 0.551 0.594 5.148 1.014 0.311 RaceBlack 0.314 1.370 1.069 0.169 11.121 0.294 0.769 RaceWhite -0.037 0.964 0.453 0.397 2.340 -0.081 0.935 Stage2 0.743 2.102 0.660 0.577 7.660 1.126 0.260 Stage3 1.503 4.494 0.678 1.189 16.977 2.216 0.027 * Stage4 2.859 17.447 0.777 3.801 80.077 3.678 0.000 *** Purity 0.146 1.157 0.770 0.256 5.228 0.189 0.850 Rsquare = 0.144 (max possible = 9.32e-01 ) Likelihood ratio test p = 9.88e-03 Wald test p = 5.1e-03 Score (logrank) test p = 3.98e-04 DHCR7 in GBM (n=153): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.098 0.906 0.118 0.719 1.143 -0.832 0.406 Age 0.031 1.032 0.009 1.015 1.049 3.660 0.000 *** Gendermale -0.088 0.916 0.213 0.603 1.391 -0.411 0.681 RaceBlack 0.573 1.774 0.729 0.425 7.397 0.786 0.432 RaceWhite -0.255 0.775 0.616 0.232 2.591 -0.414 0.679 Purity -0.968 0.380 0.557 0.128 1.131 -1.740 0.082 · Rsquare = 0.133 (max possible = 9.98e-01 ) Likelihood ratio test p = 3.64e-03 Wald test p = 6e-03 Score (logrank) test p = 5.06e-03 DHCR7 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.105 1.111 0.060 0.989 1.249 1.766 0.077 · Age 0.022 1.022 0.008 1.007 1.037 2.811 0.005 ** Gendermale -0.275 0.760 0.172 0.543 1.064 -1.600 0.110 RaceBlack 0.050 1.052 0.561 0.350 3.156 0.090 0.928 RaceWhite -0.297 0.743 0.512 0.273 2.026 -0.580 0.562 Stage2 0.660 1.935 0.544 0.666 5.620 1.213 0.225 Stage3 0.899 2.457 0.537 0.857 7.045 1.673 0.094 · Stage4 1.265 3.543 0.510 1.304 9.625 2.480 0.013 * Purity -0.127 0.881 0.370 0.427 1.817 -0.344 0.731 Rsquare = 0.076 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.55e-04 Wald test p = 5.28e-04 Score (logrank) test p = 3.61e-04 DHCR7 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.540 1.717000e+00 0.217 1.122 2.626 2.493 0.013 * Age 0.003 1.003000e+00 0.025 0.955 1.053 0.102 0.919 Gendermale -0.455 6.340000e-01 0.561 0.211 1.905 -0.811 0.417 RaceBlack 18.442 1.021647e+08 14335.233 0.000 Inf 0.001 0.999 RaceWhite 17.759 5.160450e+07 14335.233 0.000 Inf 0.001 0.999 Stage2 17.493 3.953882e+07 6160.795 0.000 Inf 0.003 0.998 Stage3 16.864 2.107957e+07 6160.795 0.000 Inf 0.003 0.998 Stage4 17.538 4.136859e+07 6160.795 0.000 Inf 0.003 0.998 Purity -1.232 2.920000e-01 1.137 0.031 2.711 -1.083 0.279 Rsquare = 0.17 (max possible = 9.17e-01 ) Likelihood ratio test p = 2.07e-01 Wald test p = 3.23e-01 Score (logrank) test p = 1.79e-01 DHCR7 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.056 1.058 0.063 0.935 1.198 0.893 0.372 Age 0.027 1.027 0.008 1.010 1.044 3.159 0.002 ** Gendermale -0.298 0.742 0.183 0.519 1.062 -1.631 0.103 RaceBlack -0.067 0.935 0.567 0.308 2.840 -0.119 0.906 RaceWhite -0.430 0.651 0.514 0.238 1.781 -0.836 0.403 Stage2 0.390 1.476 0.554 0.498 4.374 0.703 0.482 Stage3 0.751 2.119 0.542 0.733 6.125 1.387 0.165 Stage4 1.149 3.156 0.512 1.157 8.606 2.245 0.025 * Purity 0.154 1.166 0.407 0.525 2.588 0.378 0.706 Rsquare = 0.087 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.48e-04 Wald test p = 8.03e-04 Score (logrank) test p = 5.81e-04 DHCR7 in KICH (n=66): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p DHCR7 0.656 1.926000e+00 0.486 0.744 4.989000e+00 1.350 0.177 Age 0.095 1.100000e+00 0.030 1.036 1.167000e+00 3.143 0.002 Gendermale -1.126 3.240000e-01 0.729 0.078 1.355000e+00 -1.544 0.123 RaceBlack -17.642 0.000000e+00 6227.611 0.000 Inf -0.003 0.998 RaceWhite -2.418 8.900000e-02 1.157 0.009 8.620000e-01 -2.089 0.037 Stage2 16.307 1.207857e+07 0.849 2288669.027 6.374529e+07 19.214 0.000 Stage3 17.536 4.129973e+07 0.780 8961918.388 1.903240e+08 22.496 0.000 Stage4 19.375 2.596930e+08 0.903 44250771.751 1.524052e+09 21.459 0.000 Purity -0.233 7.920000e-01 3.291 0.001 5.007880e+02 -0.071 0.944 signif DHCR7 Age ** Gendermale RaceBlack RaceWhite * Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.357 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.01e-03 Wald test p = 6.2e-286 Score (logrank) test p = 8.43e-09 DHCR7 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.058 1.059 0.143 0.800 1.403 0.403 0.687 Age 0.035 1.036 0.008 1.019 1.053 4.179 0.000 *** Gendermale -0.081 0.922 0.183 0.643 1.321 -0.443 0.658 RaceBlack 0.173 1.189 1.059 0.149 9.467 0.163 0.870 RaceWhite 0.113 1.120 1.019 0.152 8.249 0.111 0.911 Stage2 0.202 1.224 0.346 0.621 2.411 0.584 0.559 Stage3 0.814 2.258 0.230 1.439 3.543 3.543 0.000 *** Stage4 1.775 5.898 0.219 3.838 9.062 8.097 0.000 *** Purity -0.026 0.974 0.373 0.469 2.021 -0.071 0.943 Rsquare = 0.174 (max possible = 9.65e-01 ) Likelihood ratio test p = 9.63e-15 Wald test p = 1.16e-14 Score (logrank) test p = 5.75e-18 DHCR7 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.297 1.346 0.329 0.706 2.564 0.903 0.367 Age 0.009 1.009 0.016 0.978 1.040 0.543 0.587 Gendermale -0.472 0.624 0.387 0.292 1.332 -1.220 0.223 RaceBlack -2.075 0.126 1.192 0.012 1.299 -1.740 0.082 · RaceWhite -2.189 0.112 1.188 0.011 1.149 -1.843 0.065 · Stage2 -0.474 0.623 1.057 0.078 4.946 -0.448 0.654 Stage3 1.615 5.029 0.427 2.179 11.607 3.785 0.000 *** Stage4 2.661 14.310 0.513 5.233 39.133 5.184 0.000 *** Purity -0.264 0.768 0.754 0.175 3.365 -0.350 0.727 Rsquare = 0.166 (max possible = 7.58e-01 ) Likelihood ratio test p = 8.27e-06 Wald test p = 3.57e-06 Score (logrank) test p = 5.31e-10 DHCR7 in LAML (n=173): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.221 1.247 0.115 0.995 1.563 1.913 0.056 · Age 0.041 1.042 0.008 1.025 1.060 4.870 0.000 *** Gendermale -0.229 0.795 0.217 0.520 1.215 -1.060 0.289 RaceBlack -0.375 0.687 1.106 0.079 6.009 -0.339 0.735 RaceWhite -0.798 0.450 1.019 0.061 3.322 -0.782 0.434 Rsquare = 0.176 (max possible = 9.96e-01 ) Likelihood ratio test p = 2.43e-05 Wald test p = 1.23e-04 Score (logrank) test p = 8.3e-05 DHCR7 in LGG (n=516): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.108 0.898 0.117 0.714 1.129 -0.925 0.355 Age 0.062 1.064 0.008 1.048 1.080 8.117 0.000 *** Gendermale 0.062 1.063 0.197 0.723 1.564 0.313 0.754 RaceBlack 15.365 4709276.834 2084.734 0.000 Inf 0.007 0.994 RaceWhite 15.408 4916506.385 2084.734 0.000 Inf 0.007 0.994 Purity -0.911 0.402 0.406 0.181 0.891 -2.244 0.025 * Rsquare = 0.138 (max possible = 9.07e-01 ) Likelihood ratio test p = 7.67e-13 Wald test p = 6.15e-13 Score (logrank) test p = 5.49e-14 DHCR7 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.026 1.027 0.098 0.847 1.244 0.267 0.790 Age 0.011 1.011 0.008 0.995 1.027 1.294 0.196 Gendermale -0.141 0.868 0.226 0.558 1.351 -0.626 0.531 RaceBlack 0.884 2.421 0.490 0.927 6.325 1.805 0.071 · RaceWhite -0.003 0.997 0.238 0.625 1.590 -0.013 0.990 Stage2 0.324 1.382 0.264 0.824 2.319 1.226 0.220 Stage3 0.946 2.575 0.235 1.625 4.082 4.025 0.000 *** Stage4 1.592 4.913 0.618 1.462 16.512 2.574 0.010 * Purity 0.559 1.749 0.463 0.706 4.335 1.207 0.227 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.17e-03 Wald test p = 6.89e-04 Score (logrank) test p = 2.63e-04 DHCR7 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.147 1.159 0.111 0.931 1.441 1.323 0.186 Age 0.008 1.008 0.009 0.991 1.026 0.929 0.353 Gendermale 0.009 1.009 0.169 0.724 1.404 0.051 0.959 RaceBlack 16.178 10617131.111 1851.129 0.000 Inf 0.009 0.993 RaceWhite 16.386 13067814.971 1851.129 0.000 Inf 0.009 0.993 Stage2 0.838 2.312 0.202 1.556 3.435 4.147 0.000 *** Stage3 0.946 2.574 0.224 1.660 3.993 4.224 0.000 *** Stage4 1.003 2.725 0.333 1.418 5.236 3.009 0.003 ** Purity 0.558 1.747 0.345 0.889 3.432 1.620 0.105 Rsquare = 0.1 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.14e-06 Wald test p = 1.75e-05 Score (logrank) test p = 2.14e-06 DHCR7 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.018 1.019 0.078 0.875 1.186 0.238 0.812 Age 0.016 1.016 0.009 0.998 1.035 1.726 0.084 · Gendermale 0.433 1.541 0.194 1.054 2.254 2.231 0.026 * RaceBlack 0.003 1.003 0.607 0.305 3.292 0.004 0.997 RaceWhite -0.524 0.592 0.563 0.196 1.787 -0.930 0.353 Stage2 0.207 1.229 0.188 0.851 1.777 1.100 0.271 Stage3 0.598 1.819 0.216 1.192 2.775 2.775 0.006 ** Stage4 0.762 2.143 0.795 0.452 10.170 0.959 0.337 Purity -0.352 0.703 0.366 0.343 1.442 -0.961 0.337 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.38e-02 Wald test p = 1.84e-02 Score (logrank) test p = 1.59e-02 DHCR7 in MESO (n=87): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.066 0.936 0.169 0.672 1.304 -0.390 0.696 Age 0.021 1.021 0.016 0.990 1.054 1.320 0.187 Gendermale -0.160 0.852 0.330 0.446 1.628 -0.485 0.628 RaceBlack 0.254 1.289 1.559 0.061 27.349 0.163 0.871 RaceWhite -0.462 0.630 1.052 0.080 4.952 -0.439 0.661 Stage2 -0.245 0.782 0.469 0.312 1.962 -0.523 0.601 Stage3 -0.129 0.879 0.424 0.383 2.017 -0.304 0.761 Stage4 -0.150 0.860 0.476 0.338 2.187 -0.316 0.752 Purity -0.731 0.482 0.551 0.163 1.419 -1.325 0.185 Rsquare = 0.062 (max possible = 9.98e-01 ) Likelihood ratio test p = 7.97e-01 Wald test p = 7.79e-01 Score (logrank) test p = 7.71e-01 DHCR7 in OV (n=303): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.002 1.002 0.117 0.797 1.260 0.018 0.986 Age 0.036 1.037 0.008 1.020 1.053 4.437 0.000 *** RaceBlack -0.051 0.950 0.585 0.302 2.988 -0.088 0.930 RaceWhite -0.156 0.856 0.519 0.309 2.367 -0.300 0.764 Purity -0.547 0.579 0.669 0.156 2.148 -0.817 0.414 Rsquare = 0.081 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.16e-03 Wald test p = 1e-03 Score (logrank) test p = 8.35e-04 DHCR7 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.072 1.074 0.142 0.814 1.418 0.507 0.612 Age 0.021 1.021 0.011 1.000 1.044 1.947 0.052 · Gendermale -0.224 0.799 0.218 0.522 1.224 -1.032 0.302 RaceBlack -0.032 0.968 0.738 0.228 4.112 -0.044 0.965 RaceWhite 0.371 1.449 0.474 0.572 3.669 0.783 0.434 Stage2 0.596 1.816 0.440 0.766 4.302 1.355 0.175 Stage3 -0.214 0.807 1.092 0.095 6.868 -0.196 0.845 Stage4 0.153 1.165 0.841 0.224 6.058 0.182 0.856 Purity -0.648 0.523 0.412 0.233 1.174 -1.571 0.116 Rsquare = 0.09 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.71e-02 Wald test p = 1.11e-01 Score (logrank) test p = 1.07e-01 DHCR7 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.122 1.130 0.625 0.332 3.844 0.196 0.845 Age 0.038 1.039 0.028 0.983 1.098 1.352 0.176 Gendermale 1.382 3.982 0.899 0.684 23.174 1.537 0.124 RaceBlack -0.363 0.695 19420.857 0.000 Inf 0.000 1.000 RaceWhite 17.202 29570439.890 15698.342 0.000 Inf 0.001 0.999 Purity 5.615 274.538 3.399 0.351 214902.402 1.652 0.099 · Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.6e-01 Wald test p = 4.02e-01 Score (logrank) test p = 3.09e-01 DHCR7 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.473 1.604 0.498 0.604 4.259 0.949 0.343 Age 0.013 1.013 0.057 0.906 1.133 0.229 0.819 RaceBlack 16.377 12957659.066 10911.943 0.000 Inf 0.002 0.999 RaceWhite 17.379 35292741.932 10911.943 0.000 Inf 0.002 0.999 Purity 1.026 2.791 1.397 0.181 43.126 0.735 0.463 Rsquare = 0.009 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.03e-01 Wald test p = 7.13e-01 Score (logrank) test p = 6.42e-01 DHCR7 in READ (n=166): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.385 1.469 0.598 0.455 4.742 0.643 0.520 Age 0.121 1.129 0.049 1.026 1.243 2.475 0.013 * Gendermale -0.375 0.687 0.679 0.181 2.601 -0.553 0.581 RaceBlack 12.707 329961.985 10202.262 0.000 Inf 0.001 0.999 RaceWhite 11.777 130245.999 10202.262 0.000 Inf 0.001 0.999 Stage2 -1.844 0.158 1.254 0.014 1.849 -1.470 0.142 Stage3 -0.319 0.727 0.932 0.117 4.511 -0.343 0.732 Stage4 -0.138 0.871 0.965 0.131 5.773 -0.143 0.886 Purity 0.152 1.164 1.317 0.088 15.382 0.115 0.908 Rsquare = 0.214 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.22e-02 Wald test p = 2.57e-01 Score (logrank) test p = 4.9e-02 DHCR7 in SARC (n=260): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.328 1.388 0.086 1.172 1.644 3.804 0.000 *** Age 0.025 1.025 0.008 1.009 1.041 3.090 0.002 ** Gendermale -0.097 0.908 0.225 0.584 1.410 -0.432 0.666 RaceBlack -0.345 0.708 1.088 0.084 5.973 -0.317 0.751 RaceWhite -0.613 0.542 1.025 0.073 4.041 -0.598 0.550 Purity 1.064 2.898 0.571 0.947 8.867 1.865 0.062 · Rsquare = 0.102 (max possible = 9.75e-01 ) Likelihood ratio test p = 3.19e-04 Wald test p = 4.3e-04 Score (logrank) test p = 3.05e-04 DHCR7 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.032 1.032 0.048 0.940 1.134 0.668 0.504 Age 0.019 1.019 0.005 1.009 1.029 3.586 0.000 *** Gendermale -0.054 0.948 0.157 0.696 1.290 -0.340 0.734 RaceWhite -1.295 0.274 0.402 0.125 0.602 -3.225 0.001 ** Stage2 0.282 1.326 0.219 0.864 2.035 1.290 0.197 Stage3 0.607 1.835 0.204 1.230 2.738 2.975 0.003 ** Stage4 1.366 3.919 0.353 1.962 7.825 3.870 0.000 *** Purity 1.030 2.802 0.341 1.436 5.466 3.022 0.003 ** Rsquare = 0.124 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.83e-08 Wald test p = 1.09e-08 Score (logrank) test p = 1.26e-09 DHCR7 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.083 1.086000e+00 0.170 0.779 1.515 0.486 0.627 Age 0.012 1.013000e+00 0.016 0.981 1.045 0.783 0.434 Gendermale 0.248 1.282000e+00 0.439 0.542 3.030 0.566 0.571 RaceWhite -1.256 2.850000e-01 0.623 0.084 0.966 -2.016 0.044 * Stage2 17.467 3.851663e+07 6221.964 0.000 Inf 0.003 0.998 Stage3 17.982 6.450295e+07 6221.964 0.000 Inf 0.003 0.998 Stage4 20.113 5.434053e+08 6221.964 0.000 Inf 0.003 0.997 Purity 0.275 1.317000e+00 0.935 0.210 8.236 0.294 0.769 Rsquare = 0.149 (max possible = 8.69e-01 ) Likelihood ratio test p = 5.67e-02 Wald test p = 4.83e-02 Score (logrank) test p = 3.94e-03 DHCR7 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.025 1.025 0.051 0.928 1.132 0.488 0.626 Age 0.021 1.021 0.006 1.010 1.032 3.678 0.000 *** Gendermale -0.062 0.940 0.172 0.670 1.317 -0.362 0.718 RaceWhite -1.068 0.344 0.600 0.106 1.114 -1.779 0.075 · Stage2 0.158 1.172 0.231 0.745 1.842 0.686 0.493 Stage3 0.559 1.749 0.209 1.161 2.635 2.674 0.007 ** Stage4 1.147 3.148 0.401 1.435 6.907 2.860 0.004 ** Purity 1.152 3.163 0.371 1.529 6.544 3.105 0.002 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 9.94e-07 Wald test p = 1.71e-06 Score (logrank) test p = 6.41e-07 DHCR7 in STAD (n=415): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.192 0.826 0.107 0.669 1.018 -1.790 0.073 · Age 0.030 1.031 0.011 1.010 1.052 2.897 0.004 ** Gendermale 0.202 1.224 0.212 0.808 1.855 0.953 0.340 RaceBlack 0.434 1.543 0.456 0.632 3.768 0.952 0.341 RaceWhite 0.139 1.149 0.245 0.710 1.858 0.565 0.572 Stage2 0.544 1.723 0.391 0.801 3.707 1.393 0.164 Stage3 0.988 2.686 0.365 1.313 5.494 2.706 0.007 ** Stage4 1.410 4.098 0.506 1.519 11.051 2.786 0.005 ** Purity -0.456 0.634 0.379 0.301 1.332 -1.203 0.229 Rsquare = 0.08 (max possible = 9.79e-01 ) Likelihood ratio test p = 4.33e-03 Wald test p = 6.39e-03 Score (logrank) test p = 4.93e-03 DHCR7 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 19.323 2.466515e+08 45088.134 0 Inf 0.000 1 Age -1.064 3.450000e-01 1777.483 0 Inf -0.001 1 RaceBlack -26.476 0.000000e+00 6052947.191 0 Inf 0.000 1 RaceWhite -61.660 0.000000e+00 23343583.035 0 Inf 0.000 1 Stage2 4.413 8.250800e+01 40209.157 0 Inf 0.000 1 Stage3 17.521 4.065528e+07 120157.828 0 Inf 0.000 1 Purity -43.102 0.000000e+00 237610.740 0 Inf 0.000 1 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 2.86e-03 DHCR7 in THCA (n=509): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.564 1.758 0.401 0.801 3.858 1.406 0.160 Age 0.157 1.170 0.030 1.104 1.241 5.250 0.000 *** Gendermale -0.141 0.869 0.639 0.248 3.040 -0.220 0.826 RaceBlack 16.270 11639931.151 5919.799 0.000 Inf 0.003 0.998 RaceWhite 16.516 14894354.624 5919.799 0.000 Inf 0.003 0.998 Stage2 0.307 1.360 1.116 0.152 12.125 0.275 0.783 Stage3 0.582 1.790 0.904 0.305 10.521 0.644 0.519 Stage4 1.895 6.650 1.019 0.903 48.967 1.860 0.063 · Purity 2.304 10.011 1.103 1.152 86.968 2.088 0.037 * Rsquare = 0.153 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.07e-10 Wald test p = 3.1e-04 Score (logrank) test p = 9.67e-11 DHCR7 in THYM (n=120): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.819 2.269 0.518 0.822 6.264 1.581 0.114 Age 0.043 1.044 0.032 0.980 1.113 1.338 0.181 Gendermale 0.031 1.032 0.758 0.233 4.560 0.041 0.967 RaceBlack -16.649 0.000 10971.545 0.000 Inf -0.002 0.999 RaceWhite 0.305 1.357 1.095 0.159 11.592 0.279 0.781 Purity 0.163 1.177 1.142 0.125 11.040 0.142 0.887 Rsquare = 0.065 (max possible = 4.51e-01 ) Likelihood ratio test p = 2.69e-01 Wald test p = 3.67e-01 Score (logrank) test p = 2.3e-01 DHCR7 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.389 1.476 0.221 0.958 2.275 1.764 0.078 · Age 0.052 1.053 0.016 1.021 1.086 3.237 0.001 ** RaceBlack -0.422 0.656 0.796 0.138 3.118 -0.531 0.596 RaceWhite -0.583 0.558 0.747 0.129 2.413 -0.781 0.435 Purity 0.325 1.384 0.652 0.385 4.972 0.499 0.618 Rsquare = 0.049 (max possible = 7.81e-01 ) Likelihood ratio test p = 1.44e-02 Wald test p = 1.91e-02 Score (logrank) test p = 1.73e-02 DHCR7 in UCS (n=57): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 -0.193 0.825 0.340 0.423 1.607 -0.566 0.571 Age 0.047 1.049 0.025 0.998 1.102 1.866 0.062 · RaceBlack 17.491 39468685.944 6482.773 0.000 Inf 0.003 0.998 RaceWhite 17.790 53211246.927 6482.773 0.000 Inf 0.003 0.998 Purity -0.747 0.474 1.080 0.057 3.935 -0.691 0.489 Rsquare = 0.124 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.28e-01 Wald test p = 3.44e-01 Score (logrank) test p = 2.5e-01 DHCR7 in UVM (n=80): Model: Surv(OS, EVENT) ~ `DHCR7` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif DHCR7 0.514 1.672 0.328 0.879 3.183 1.566 0.117 Age 0.033 1.034 0.019 0.996 1.073 1.765 0.078 · Gendermale 0.379 1.461 0.506 0.542 3.939 0.749 0.454 Stage3 0.039 1.040 0.529 0.369 2.934 0.074 0.941 Stage4 3.657 38.739 1.214 3.586 418.520 3.012 0.003 ** Purity 1.886 6.594 1.222 0.601 72.327 1.544 0.123 Rsquare = 0.277 (max possible = 8.72e-01 ) Likelihood ratio test p = 3.46e-04 Wald test p = 1.62e-03 Score (logrank) test p = 9.91e-10 FDFT1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.270 1.309 0.175 0.929 1.846 1.538 0.124 Age 0.010 1.010 0.014 0.982 1.038 0.671 0.502 Gendermale 0.568 1.765 0.438 0.748 4.165 1.297 0.195 RaceBlack -0.413 0.662 12292.167 0.000 Inf 0.000 1.000 RaceWhite 16.343 12516824.216 10497.623 0.000 Inf 0.002 0.999 Purity 2.243 9.424 2.343 0.096 929.661 0.958 0.338 Rsquare = 0.103 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.25e-01 Wald test p = 5.97e-01 Score (logrank) test p = 4.43e-01 FDFT1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.002 0.998 0.089 0.838 1.188 -0.026 0.980 Age 0.033 1.034 0.009 1.017 1.052 3.860 0.000 *** Gendermale -0.171 0.843 0.178 0.594 1.196 -0.959 0.338 RaceBlack 0.711 2.035 0.446 0.849 4.878 1.593 0.111 RaceWhite 0.116 1.123 0.355 0.560 2.250 0.327 0.744 Stage2 14.513 2007918.759 1864.042 0.000 Inf 0.008 0.994 Stage3 14.947 3100326.815 1864.042 0.000 Inf 0.008 0.994 Stage4 15.489 5328622.088 1864.042 0.000 Inf 0.008 0.993 Purity 0.144 1.155 0.353 0.578 2.308 0.409 0.683 Rsquare = 0.13 (max possible = 9.91e-01 ) Likelihood ratio test p = 2.02e-07 Wald test p = 1.24e-06 Score (logrank) test p = 3.3e-07 FDFT1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.093 1.097 0.137 0.839 1.435 0.676 0.499 Age 0.036 1.036 0.008 1.021 1.052 4.727 0.000 *** Gendermale 0.035 1.035 1.007 0.144 7.456 0.034 0.972 RaceBlack 0.014 1.014 0.619 0.301 3.411 0.022 0.982 RaceWhite -0.238 0.788 0.596 0.245 2.536 -0.399 0.690 Stage2 0.403 1.497 0.304 0.826 2.714 1.328 0.184 Stage3 1.180 3.255 0.313 1.763 6.012 3.771 0.000 *** Stage4 2.452 11.611 0.400 5.306 25.407 6.137 0.000 *** Purity 0.504 1.655 0.422 0.724 3.783 1.195 0.232 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.02e-12 Wald test p = 4.44e-16 Score (logrank) test p = 6.34e-22 FDFT1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.282 7.550000e-01 0.335 0.391 1.456 -0.840 0.401 Age 0.015 1.015000e+00 0.018 0.980 1.051 0.817 0.414 RaceBlack -0.757 4.690000e-01 1.124 0.052 4.246 -0.673 0.501 RaceWhite -1.014 3.630000e-01 1.145 0.038 3.424 -0.885 0.376 Stage2 18.736 1.371312e+08 6481.120 0.000 Inf 0.003 0.998 Stage3 20.139 5.577795e+08 6481.120 0.000 Inf 0.003 0.998 Stage4 21.507 2.190522e+09 6481.120 0.000 Inf 0.003 0.997 Purity 0.647 1.909000e+00 0.971 0.285 12.799 0.666 0.505 Rsquare = 0.16 (max possible = 7.18e-01 ) Likelihood ratio test p = 3.92e-04 Wald test p = 7.06e-03 Score (logrank) test p = 4.05e-06 FDFT1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.389 1.475000e+00 0.540 0.511 4.253 0.719 0.472 Age 0.027 1.027000e+00 0.029 0.971 1.087 0.929 0.353 RaceBlack -3.211 4.000000e-02 1.836 0.001 1.473 -1.749 0.080 · RaceWhite -1.695 1.840000e-01 1.434 0.011 3.051 -1.182 0.237 Stage2 18.248 8.410847e+07 14678.481 0.000 Inf 0.001 0.999 Stage3 19.870 4.258103e+08 14678.481 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.273 2.637800e+01 2.333 0.272 2555.748 1.402 0.161 Rsquare = 0.375 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.42e-04 Wald test p = 2.27e-01 Score (logrank) test p = 1.01e-14 FDFT1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.439 1.552 0.205 1.037 2.320 2.139 0.032 * Age 0.049 1.050 0.012 1.026 1.075 4.107 0.000 *** Gendermale -15.314 0.000 3492.570 0.000 Inf -0.004 0.997 RaceBlack -0.365 0.694 1.173 0.070 6.912 -0.311 0.755 RaceWhite 0.162 1.176 1.034 0.155 8.927 0.157 0.875 Stage2 0.300 1.350 0.374 0.649 2.807 0.803 0.422 Stage3 0.919 2.508 0.395 1.157 5.436 2.330 0.020 * Stage4 2.019 7.533 0.595 2.345 24.203 3.391 0.001 ** Purity 0.211 1.235 0.615 0.370 4.122 0.343 0.732 Rsquare = 0.079 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.78e-05 Wald test p = 2.1e-06 Score (logrank) test p = 4.25e-08 FDFT1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.220 0.803 0.361 0.395 1.630 -0.608 0.543 Age 0.050 1.051 0.021 1.009 1.095 2.404 0.016 * Gendermale 1.063 2.894 1.118 0.324 25.887 0.951 0.342 RaceBlack 16.440 13791235.938 6439.109 0.000 Inf 0.003 0.998 RaceWhite 15.955 8495917.483 6439.109 0.000 Inf 0.002 0.998 Stage2 0.740 2.096 1.076 0.254 17.266 0.688 0.492 Stage3 1.788 5.977 1.110 0.679 52.598 1.611 0.107 Stage4 2.358 10.572 1.250 0.913 122.414 1.887 0.059 · Purity 0.990 2.691 1.305 0.209 34.719 0.758 0.448 Rsquare = 0.107 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.72e-02 Wald test p = 7.4e-02 Score (logrank) test p = 2.38e-02 FDFT1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.138 0.871 0.155 0.642 1.181 -0.889 0.374 Age 0.013 1.013 0.010 0.993 1.034 1.317 0.188 RaceBlack 1.021 2.776 1.069 0.342 22.568 0.955 0.339 RaceWhite 0.774 2.168 1.017 0.296 15.903 0.761 0.446 Purity 0.608 1.836 0.739 0.431 7.819 0.822 0.411 Rsquare = 0.017 (max possible = 8.91e-01 ) Likelihood ratio test p = 5.62e-01 Wald test p = 5.94e-01 Score (logrank) test p = 5.87e-01 FDFT1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.013 1.013 0.444 0.424 2.420 0.028 0.977 Age 0.018 1.018 0.022 0.975 1.063 0.803 0.422 Gendermale 0.249 1.283 0.572 0.418 3.939 0.436 0.663 RaceBlack -0.342 0.710 1.511 0.037 13.715 -0.227 0.821 RaceWhite -1.070 0.343 0.902 0.059 2.009 -1.186 0.236 Stage2 0.657 1.929 0.676 0.513 7.258 0.972 0.331 Stage3 -15.526 0.000 6957.027 0.000 Inf -0.002 0.998 Stage4 0.821 2.272 0.676 0.604 8.553 1.213 0.225 Purity 2.046 7.734 1.598 0.337 177.360 1.280 0.201 Rsquare = 0.211 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.8e-01 Wald test p = 6.52e-01 Score (logrank) test p = 4.84e-01 FDFT1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.290 0.748 0.189 0.517 1.083 -1.539 0.124 Age 0.024 1.025 0.012 1.002 1.048 2.108 0.035 * Gendermale 0.163 1.178 0.272 0.691 2.008 0.600 0.548 RaceBlack -0.583 0.558 0.829 0.110 2.836 -0.703 0.482 RaceWhite -0.638 0.528 0.782 0.114 2.449 -0.815 0.415 Stage2 0.129 1.138 0.565 0.376 3.447 0.229 0.819 Stage3 0.649 1.913 0.560 0.638 5.735 1.158 0.247 Stage4 1.693 5.437 0.566 1.793 16.485 2.992 0.003 ** Purity -0.362 0.696 0.603 0.214 2.269 -0.601 0.548 Rsquare = 0.118 (max possible = 9.04e-01 ) Likelihood ratio test p = 1.8e-04 Wald test p = 5.72e-05 Score (logrank) test p = 8.08e-06 FDFT1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -1.192 0.304 1.248 0.026 3.507 -0.955 0.340 Age -0.010 0.990 0.046 0.904 1.084 -0.219 0.827 Gendermale 0.636 1.890 1.077 0.229 15.602 0.591 0.555 RaceBlack -0.377 0.686 1.845 0.018 25.489 -0.205 0.838 RaceWhite -2.348 0.096 1.362 0.007 1.379 -1.724 0.085 · Purity -2.257 0.105 2.351 0.001 10.494 -0.960 0.337 Rsquare = 0.152 (max possible = 5.58e-01 ) Likelihood ratio test p = 3.42e-01 Wald test p = 5.87e-01 Score (logrank) test p = 2.91e-01 FDFT1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.064 0.938 0.131 0.725 1.214 -0.485 0.628 Age 0.009 1.009 0.014 0.982 1.037 0.641 0.521 Gendermale 0.496 1.642 0.538 0.573 4.711 0.923 0.356 RaceBlack 0.452 1.572 1.094 0.184 13.412 0.414 0.679 RaceWhite -0.063 0.939 0.447 0.391 2.256 -0.141 0.888 Stage2 0.645 1.907 0.660 0.523 6.958 0.977 0.328 Stage3 1.422 4.145 0.671 1.112 15.445 2.118 0.034 * Stage4 2.839 17.094 0.775 3.745 78.027 3.664 0.000 *** Purity 0.256 1.292 0.778 0.281 5.933 0.329 0.742 Rsquare = 0.142 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.06e-02 Wald test p = 4.91e-03 Score (logrank) test p = 4.07e-04 FDFT1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.075 0.927 0.193 0.636 1.353 -0.392 0.695 Age 0.029 1.030 0.008 1.013 1.047 3.522 0.000 *** Gendermale -0.079 0.924 0.217 0.604 1.413 -0.365 0.715 RaceBlack 0.577 1.780 0.736 0.420 7.538 0.783 0.434 RaceWhite -0.216 0.806 0.617 0.240 2.699 -0.350 0.726 Purity -1.043 0.352 0.547 0.121 1.029 -1.907 0.057 · Rsquare = 0.13 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.52e-03 Wald test p = 6.95e-03 Score (logrank) test p = 5.94e-03 FDFT1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.093 0.911 0.102 0.747 1.111 -0.919 0.358 Age 0.023 1.024 0.008 1.008 1.039 3.006 0.003 ** Gendermale -0.242 0.785 0.172 0.561 1.100 -1.405 0.160 RaceBlack 0.198 1.219 0.563 0.404 3.676 0.351 0.725 RaceWhite -0.228 0.796 0.511 0.292 2.168 -0.447 0.655 Stage2 0.626 1.870 0.544 0.644 5.428 1.151 0.250 Stage3 0.858 2.357 0.537 0.823 6.750 1.598 0.110 Stage4 1.278 3.591 0.511 1.320 9.770 2.504 0.012 * Purity 0.031 1.031 0.372 0.498 2.136 0.083 0.934 Rsquare = 0.071 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.79e-04 Wald test p = 1.03e-03 Score (logrank) test p = 7.57e-04 FDFT1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.832 4.350000e-01 0.318 0.233 0.812 -2.614 0.009 ** Age 0.012 1.012000e+00 0.027 0.959 1.068 0.434 0.664 Gendermale 0.037 1.037000e+00 0.556 0.349 3.084 0.066 0.948 RaceBlack 19.836 4.119114e+08 21486.316 0.000 Inf 0.001 0.999 RaceWhite 18.996 1.778520e+08 21486.315 0.000 Inf 0.001 0.999 Stage2 18.239 8.339080e+07 8350.642 0.000 Inf 0.002 0.998 Stage3 17.314 3.307378e+07 8350.642 0.000 Inf 0.002 0.998 Stage4 18.202 8.038214e+07 8350.642 0.000 Inf 0.002 0.998 Purity -0.761 4.670000e-01 1.053 0.059 3.677 -0.723 0.470 Rsquare = 0.179 (max possible = 9.17e-01 ) Likelihood ratio test p = 1.72e-01 Wald test p = 3.92e-01 Score (logrank) test p = 2.79e-01 FDFT1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.012 0.988 0.112 0.793 1.230 -0.112 0.911 Age 0.027 1.027 0.009 1.010 1.045 3.133 0.002 ** Gendermale -0.284 0.753 0.183 0.526 1.077 -1.554 0.120 RaceBlack -0.008 0.992 0.570 0.325 3.031 -0.014 0.989 RaceWhite -0.394 0.674 0.513 0.247 1.842 -0.769 0.442 Stage2 0.368 1.446 0.554 0.488 4.281 0.665 0.506 Stage3 0.729 2.072 0.541 0.718 5.985 1.347 0.178 Stage4 1.151 3.163 0.513 1.156 8.650 2.243 0.025 * Purity 0.219 1.245 0.409 0.559 2.774 0.537 0.592 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.39e-04 Wald test p = 9.47e-04 Score (logrank) test p = 7.17e-04 FDFT1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p FDFT1 0.536 1.709000e+00 0.436 0.727 4.014000e+00 1.230 0.219 Age 0.079 1.082000e+00 0.029 1.022 1.147000e+00 2.682 0.007 Gendermale -0.861 4.230000e-01 0.727 0.102 1.756000e+00 -1.185 0.236 RaceBlack -16.500 0.000000e+00 6141.123 0.000 Inf -0.003 0.998 RaceWhite -1.409 2.440000e-01 1.156 0.025 2.355000e+00 -1.219 0.223 Stage2 15.854 7.677224e+06 0.846 1462007.921 4.031426e+07 18.736 0.000 Stage3 16.930 2.252236e+07 0.778 4903931.392 1.034388e+08 21.766 0.000 Stage4 19.097 1.966049e+08 0.893 34128597.779 1.132584e+09 21.375 0.000 Purity 0.067 1.069000e+00 3.255 0.002 6.311090e+02 0.021 0.984 signif FDFT1 Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.356 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.05e-03 Wald test p = 8.65e-273 Score (logrank) test p = 8.39e-09 FDFT1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.436 0.646 0.146 0.485 0.861 -2.979 0.003 ** Age 0.033 1.034 0.008 1.017 1.051 3.960 0.000 *** Gendermale -0.071 0.931 0.185 0.648 1.338 -0.385 0.701 RaceBlack 0.374 1.453 1.059 0.182 11.578 0.353 0.724 RaceWhite 0.353 1.423 1.018 0.194 10.467 0.347 0.729 Stage2 0.213 1.237 0.345 0.629 2.432 0.617 0.537 Stage3 0.696 2.005 0.233 1.270 3.168 2.983 0.003 ** Stage4 1.679 5.361 0.217 3.503 8.203 7.735 0.000 *** Purity -0.145 0.865 0.370 0.419 1.785 -0.392 0.695 Rsquare = 0.19 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.94e-16 Wald test p = 1.22e-16 Score (logrank) test p = 4.91e-20 FDFT1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.756 2.130 0.344 1.085 4.183 2.197 0.028 * Age 0.009 1.009 0.015 0.979 1.040 0.603 0.547 Gendermale -0.311 0.733 0.395 0.338 1.590 -0.787 0.431 RaceBlack -2.481 0.084 1.202 0.008 0.883 -2.063 0.039 * RaceWhite -2.518 0.081 1.184 0.008 0.821 -2.127 0.033 * Stage2 -0.386 0.680 1.056 0.086 5.383 -0.366 0.715 Stage3 1.558 4.751 0.427 2.058 10.969 3.650 0.000 *** Stage4 2.894 18.057 0.528 6.409 50.872 5.475 0.000 *** Purity -0.350 0.705 0.755 0.160 3.096 -0.464 0.643 Rsquare = 0.181 (max possible = 7.58e-01 ) Likelihood ratio test p = 1.58e-06 Wald test p = 1.06e-06 Score (logrank) test p = 1.44e-10 FDFT1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.200 1.222 0.245 0.756 1.974 0.818 0.413 Age 0.039 1.039 0.008 1.023 1.056 4.692 0.000 *** Gendermale -0.164 0.849 0.215 0.558 1.293 -0.762 0.446 RaceBlack -0.287 0.751 1.108 0.086 6.588 -0.259 0.796 RaceWhite -0.680 0.507 1.018 0.069 3.727 -0.668 0.504 Rsquare = 0.159 (max possible = 9.96e-01 ) Likelihood ratio test p = 9.4e-05 Wald test p = 3.65e-04 Score (logrank) test p = 2.45e-04 FDFT1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.579 0.561 0.161 0.409 0.768 -3.602 0.000 *** Age 0.063 1.065 0.008 1.049 1.081 8.093 0.000 *** Gendermale -0.027 0.974 0.197 0.662 1.433 -0.135 0.892 RaceBlack 15.145 3779864.878 2219.401 0.000 Inf 0.007 0.995 RaceWhite 15.205 4012116.118 2219.401 0.000 Inf 0.007 0.995 Purity -0.839 0.432 0.413 0.192 0.971 -2.031 0.042 * Rsquare = 0.159 (max possible = 9.07e-01 ) Likelihood ratio test p = 3.39e-15 Wald test p = 2.41e-15 Score (logrank) test p = 9.7e-17 FDFT1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.201 1.223 0.125 0.958 1.561 1.616 0.106 Age 0.013 1.013 0.008 0.997 1.029 1.545 0.122 Gendermale -0.156 0.855 0.227 0.548 1.334 -0.689 0.491 RaceBlack 0.843 2.324 0.490 0.890 6.067 1.723 0.085 · RaceWhite 0.039 1.040 0.238 0.652 1.657 0.163 0.870 Stage2 0.324 1.383 0.261 0.828 2.308 1.240 0.215 Stage3 0.889 2.433 0.238 1.527 3.875 3.743 0.000 *** Stage4 1.687 5.401 0.622 1.596 18.273 2.712 0.007 ** Purity 0.487 1.627 0.459 0.662 3.999 1.062 0.288 Rsquare = 0.092 (max possible = 9.66e-01 ) Likelihood ratio test p = 4.34e-04 Wald test p = 2.66e-04 Score (logrank) test p = 9.66e-05 FDFT1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.069 1.072 0.121 0.845 1.359 0.569 0.569 Age 0.007 1.007 0.009 0.989 1.025 0.735 0.463 Gendermale 0.017 1.017 0.169 0.730 1.416 0.098 0.922 RaceBlack 16.097 9790121.359 1872.183 0.000 Inf 0.009 0.993 RaceWhite 16.272 11666312.564 1872.183 0.000 Inf 0.009 0.993 Stage2 0.867 2.381 0.201 1.605 3.531 4.313 0.000 *** Stage3 1.011 2.748 0.218 1.793 4.212 4.640 0.000 *** Stage4 0.987 2.684 0.335 1.391 5.179 2.944 0.003 ** Purity 0.589 1.803 0.343 0.921 3.531 1.719 0.086 · Rsquare = 0.097 (max possible = 9.74e-01 ) Likelihood ratio test p = 2.09e-06 Wald test p = 2.91e-05 Score (logrank) test p = 3.43e-06 FDFT1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.028 1.028 0.101 0.844 1.253 0.276 0.782 Age 0.016 1.016 0.009 0.998 1.035 1.702 0.089 · Gendermale 0.434 1.543 0.194 1.056 2.255 2.241 0.025 * RaceBlack 0.006 1.006 0.608 0.306 3.313 0.010 0.992 RaceWhite -0.516 0.597 0.565 0.197 1.807 -0.913 0.361 Stage2 0.209 1.233 0.187 0.855 1.778 1.121 0.262 Stage3 0.607 1.835 0.215 1.205 2.794 2.827 0.005 ** Stage4 0.752 2.120 0.798 0.444 10.132 0.942 0.346 Purity -0.356 0.700 0.367 0.341 1.438 -0.970 0.332 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.36e-02 Wald test p = 1.84e-02 Score (logrank) test p = 1.58e-02 FDFT1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.874 2.396 0.244 1.486 3.862 3.585 0.000 *** Age 0.010 1.010 0.016 0.980 1.042 0.658 0.511 Gendermale -0.190 0.827 0.340 0.425 1.609 -0.559 0.576 RaceBlack -0.425 0.654 1.528 0.033 13.060 -0.278 0.781 RaceWhite -0.489 0.613 1.045 0.079 4.755 -0.468 0.640 Stage2 -0.199 0.820 0.459 0.333 2.016 -0.433 0.665 Stage3 -0.445 0.641 0.436 0.273 1.505 -1.022 0.307 Stage4 -0.353 0.702 0.476 0.276 1.787 -0.741 0.458 Purity -0.569 0.566 0.593 0.177 1.812 -0.958 0.338 Rsquare = 0.197 (max possible = 9.98e-01 ) Likelihood ratio test p = 2.85e-02 Wald test p = 3.98e-02 Score (logrank) test p = 3.13e-02 FDFT1 in OV (n=303): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.119 1.126 0.097 0.931 1.363 1.225 0.221 Age 0.039 1.040 0.008 1.023 1.057 4.595 0.000 *** RaceBlack 0.052 1.053 0.583 0.336 3.303 0.089 0.929 RaceWhite -0.068 0.934 0.520 0.337 2.590 -0.131 0.896 Purity -0.577 0.562 0.674 0.150 2.104 -0.856 0.392 Rsquare = 0.087 (max possible = 9.97e-01 ) Likelihood ratio test p = 5.97e-04 Wald test p = 5.3e-04 Score (logrank) test p = 4.4e-04 FDFT1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.197 1.218 0.161 0.889 1.669 1.227 0.220 Age 0.020 1.020 0.011 0.999 1.042 1.843 0.065 · Gendermale -0.210 0.810 0.217 0.530 1.240 -0.969 0.333 RaceBlack -0.034 0.967 0.738 0.228 4.106 -0.046 0.963 RaceWhite 0.363 1.438 0.473 0.570 3.632 0.769 0.442 Stage2 0.502 1.653 0.446 0.689 3.964 1.126 0.260 Stage3 -0.317 0.728 1.093 0.085 6.210 -0.290 0.772 Stage4 0.010 1.010 0.844 0.193 5.276 0.012 0.991 Purity -0.635 0.530 0.413 0.236 1.189 -1.540 0.124 Rsquare = 0.097 (max possible = 9.91e-01 ) Likelihood ratio test p = 5.19e-02 Wald test p = 8.17e-02 Score (logrank) test p = 7.88e-02 FDFT1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.029 1.029 0.734 0.244 4.335 0.039 0.969 Age 0.038 1.039 0.030 0.980 1.102 1.277 0.201 Gendermale 1.402 4.064 0.902 0.694 23.794 1.555 0.120 RaceBlack -0.269 0.764 19665.728 0.000 Inf 0.000 1.000 RaceWhite 17.228 30350648.047 15711.973 0.000 Inf 0.001 0.999 Purity 5.649 283.868 3.453 0.326 246979.084 1.636 0.102 Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.62e-01 Wald test p = 4.15e-01 Score (logrank) test p = 3.11e-01 FDFT1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.313 1.368 0.364 0.670 2.793 0.861 0.389 Age 0.010 1.010 0.056 0.905 1.127 0.178 0.859 RaceBlack 15.030 3369182.589 6731.357 0.000 Inf 0.002 0.998 RaceWhite 16.224 11113397.404 6731.357 0.000 Inf 0.002 0.998 Purity 1.280 3.597 1.430 0.218 59.251 0.895 0.371 Rsquare = 0.008 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.33e-01 Wald test p = 7.46e-01 Score (logrank) test p = 6.75e-01 FDFT1 in READ (n=166): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.192 1.211 0.422 0.530 2.768 0.454 0.649 Age 0.115 1.122 0.046 1.025 1.229 2.503 0.012 * Gendermale -0.405 0.667 0.692 0.172 2.591 -0.585 0.559 RaceBlack 13.029 455645.189 10198.036 0.000 Inf 0.001 0.999 RaceWhite 11.990 161108.669 10198.036 0.000 Inf 0.001 0.999 Stage2 -1.860 0.156 1.257 0.013 1.828 -1.480 0.139 Stage3 -0.354 0.702 0.938 0.111 4.414 -0.378 0.706 Stage4 -0.152 0.859 0.955 0.132 5.580 -0.159 0.873 Purity 0.295 1.343 1.362 0.093 19.387 0.217 0.828 Rsquare = 0.211 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.46e-02 Wald test p = 2.55e-01 Score (logrank) test p = 4.91e-02 FDFT1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.188 1.207 0.147 0.905 1.611 1.280 0.201 Age 0.023 1.023 0.008 1.006 1.040 2.719 0.007 ** Gendermale 0.004 1.004 0.223 0.649 1.554 0.018 0.986 RaceBlack -0.106 0.899 1.086 0.107 7.554 -0.098 0.922 RaceWhite -0.449 0.638 1.023 0.086 4.740 -0.439 0.661 Purity 0.746 2.109 0.591 0.662 6.714 1.263 0.207 Rsquare = 0.049 (max possible = 9.75e-01 ) Likelihood ratio test p = 6.72e-02 Wald test p = 1e-01 Score (logrank) test p = 9.66e-02 FDFT1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.002 1.002 0.096 0.831 1.208 0.020 0.984 Age 0.018 1.019 0.005 1.008 1.029 3.547 0.000 *** Gendermale -0.050 0.951 0.157 0.699 1.295 -0.319 0.749 RaceWhite -1.287 0.276 0.408 0.124 0.614 -3.158 0.002 ** Stage2 0.275 1.317 0.218 0.858 2.020 1.260 0.208 Stage3 0.611 1.842 0.204 1.235 2.748 2.994 0.003 ** Stage4 1.351 3.860 0.352 1.936 7.695 3.837 0.000 *** Purity 1.017 2.766 0.352 1.386 5.519 2.887 0.004 ** Rsquare = 0.123 (max possible = 9.92e-01 ) Likelihood ratio test p = 2.23e-08 Wald test p = 1.19e-08 Score (logrank) test p = 1.42e-09 FDFT1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.268 1.307000e+00 0.254 0.795 2.149 1.056 0.291 Age 0.014 1.014000e+00 0.016 0.982 1.047 0.830 0.407 Gendermale 0.258 1.294000e+00 0.436 0.550 3.044 0.591 0.555 RaceWhite -1.395 2.480000e-01 0.651 0.069 0.888 -2.143 0.032 * Stage2 17.605 4.424485e+07 6197.232 0.000 Inf 0.003 0.998 Stage3 18.157 7.681477e+07 6197.232 0.000 Inf 0.003 0.998 Stage4 20.409 7.302513e+08 6197.232 0.000 Inf 0.003 0.997 Purity 0.015 1.015000e+00 0.981 0.149 6.943 0.016 0.988 Rsquare = 0.157 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.17e-02 Wald test p = 3.54e-02 Score (logrank) test p = 2.63e-03 FDFT1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.014 0.986 0.105 0.803 1.210 -0.136 0.892 Age 0.021 1.021 0.006 1.010 1.032 3.656 0.000 *** Gendermale -0.057 0.944 0.172 0.674 1.323 -0.333 0.739 RaceWhite -1.045 0.352 0.605 0.107 1.150 -1.729 0.084 · Stage2 0.151 1.163 0.230 0.740 1.827 0.656 0.512 Stage3 0.563 1.756 0.209 1.166 2.645 2.695 0.007 ** Stage4 1.132 3.100 0.400 1.416 6.791 2.829 0.005 ** Purity 1.158 3.184 0.385 1.498 6.767 3.010 0.003 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 1.09e-06 Wald test p = 1.76e-06 Score (logrank) test p = 6.7e-07 FDFT1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.169 0.845 0.109 0.683 1.046 -1.550 0.121 Age 0.029 1.029 0.010 1.009 1.051 2.790 0.005 ** Gendermale 0.132 1.141 0.208 0.759 1.714 0.634 0.526 RaceBlack 0.250 1.283 0.447 0.534 3.085 0.558 0.577 RaceWhite 0.133 1.142 0.245 0.707 1.846 0.544 0.586 Stage2 0.497 1.644 0.390 0.766 3.531 1.275 0.202 Stage3 0.903 2.466 0.363 1.210 5.027 2.483 0.013 * Stage4 1.395 4.036 0.505 1.500 10.861 2.762 0.006 ** Purity -0.546 0.579 0.381 0.275 1.222 -1.433 0.152 Rsquare = 0.077 (max possible = 9.79e-01 ) Likelihood ratio test p = 5.58e-03 Wald test p = 8.1e-03 Score (logrank) test p = 6.56e-03 FDFT1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -2.187 0.112 29900.838 0 Inf 0.000 1.000 Age -1.786 0.168 1625.592 0 Inf -0.001 0.999 RaceBlack -1.209 0.298 21203853.200 0 Inf 0.000 1.000 RaceWhite -47.361 0.000 23386602.794 0 Inf 0.000 1.000 Stage2 -2.338 0.097 38460.880 0 Inf 0.000 1.000 Stage3 17.505 40042845.051 179241.506 0 Inf 0.000 1.000 Purity 10.727 45581.746 214626.975 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.01e-03 FDFT1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 2.035 7.653 0.754 1.746 33.536 2.700 0.007 ** Age 0.163 1.177 0.030 1.110 1.248 5.462 0.000 *** Gendermale 0.102 1.108 0.626 0.325 3.778 0.163 0.870 RaceBlack 17.181 28934091.777 9038.525 0.000 Inf 0.002 0.998 RaceWhite 17.613 44592147.089 9038.525 0.000 Inf 0.002 0.998 Stage2 0.119 1.126 1.055 0.142 8.899 0.112 0.911 Stage3 0.344 1.411 0.840 0.272 7.316 0.410 0.682 Stage4 1.925 6.852 0.953 1.057 44.407 2.019 0.044 * Purity 2.124 8.361 1.035 1.100 63.539 2.052 0.040 * Rsquare = 0.165 (max possible = 3.47e-01 ) Likelihood ratio test p = 8.21e-12 Wald test p = 4.99e-05 Score (logrank) test p = 4.37e-11 FDFT1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -1.129 0.323 0.738 0.076 1.373 -1.531 0.126 Age 0.037 1.038 0.031 0.976 1.103 1.196 0.232 Gendermale -0.354 0.702 0.766 0.156 3.153 -0.461 0.645 RaceBlack -15.775 0.000 10204.637 0.000 Inf -0.002 0.999 RaceWhite 0.949 2.584 1.153 0.270 24.766 0.823 0.410 Purity 0.372 1.450 1.130 0.158 13.289 0.329 0.742 Rsquare = 0.064 (max possible = 4.51e-01 ) Likelihood ratio test p = 2.76e-01 Wald test p = 3.81e-01 Score (logrank) test p = 2.64e-01 FDFT1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.189 1.208 0.203 0.811 1.800 0.931 0.352 Age 0.051 1.053 0.016 1.020 1.086 3.235 0.001 ** RaceBlack -0.365 0.694 0.796 0.146 3.301 -0.459 0.646 RaceWhite -0.508 0.602 0.746 0.139 2.598 -0.680 0.496 Purity 0.492 1.635 0.651 0.456 5.858 0.755 0.450 Rsquare = 0.041 (max possible = 7.81e-01 ) Likelihood ratio test p = 3.58e-02 Wald test p = 4.25e-02 Score (logrank) test p = 4.25e-02 FDFT1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 0.065 1.067 0.263 0.638 1.786 0.248 0.804 Age 0.046 1.047 0.026 0.995 1.101 1.778 0.075 · RaceBlack 17.714 49344234.839 6477.658 0.000 Inf 0.003 0.998 RaceWhite 17.927 61034295.037 6477.658 0.000 Inf 0.003 0.998 Purity -0.791 0.453 1.100 0.052 3.916 -0.719 0.472 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.48e-01 Wald test p = 3.58e-01 Score (logrank) test p = 2.63e-01 FDFT1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `FDFT1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDFT1 -0.729 0.482 0.269 0.285 0.817 -2.712 0.007 ** Age 0.034 1.035 0.021 0.994 1.078 1.674 0.094 · Gendermale 0.215 1.240 0.482 0.482 3.187 0.447 0.655 Stage3 0.343 1.409 0.500 0.529 3.758 0.686 0.493 Stage4 3.539 34.442 1.235 3.058 387.872 2.865 0.004 ** Purity 1.685 5.390 1.268 0.449 64.709 1.329 0.184 Rsquare = 0.318 (max possible = 8.72e-01 ) Likelihood ratio test p = 4.84e-05 Wald test p = 4.91e-04 Score (logrank) test p = 1.63e-10 FDPS in ACC (n=79): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.332 1.394 0.135 1.070 1.817 2.459 0.014 * Age 0.015 1.015 0.014 0.987 1.044 1.056 0.291 Gendermale 0.792 2.208 0.453 0.910 5.362 1.751 0.080 · RaceBlack -0.732 0.481 12802.440 0.000 Inf 0.000 1.000 RaceWhite 16.051 9349218.118 10946.972 0.000 Inf 0.001 0.999 Purity 2.249 9.474 2.246 0.116 773.518 1.001 0.317 Rsquare = 0.157 (max possible = 9.38e-01 ) Likelihood ratio test p = 9.07e-02 Wald test p = 2.15e-01 Score (logrank) test p = 1.36e-01 FDPS in BLCA (n=408): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.078 1.081 0.129 0.839 1.393 0.604 0.546 Age 0.033 1.034 0.009 1.017 1.051 3.864 0.000 *** Gendermale -0.179 0.836 0.179 0.589 1.187 -1.001 0.317 RaceBlack 0.694 2.002 0.447 0.834 4.807 1.554 0.120 RaceWhite 0.105 1.111 0.355 0.554 2.226 0.296 0.767 Stage2 14.467 1918968.947 1863.525 0.000 Inf 0.008 0.994 Stage3 14.918 3010984.151 1863.525 0.000 Inf 0.008 0.994 Stage4 15.435 5051316.103 1863.525 0.000 Inf 0.008 0.993 Purity 0.129 1.138 0.340 0.584 2.218 0.380 0.704 Rsquare = 0.131 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.73e-07 Wald test p = 1.06e-06 Score (logrank) test p = 3.06e-07 FDPS in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.044 1.045 0.170 0.749 1.458 0.257 0.797 Age 0.036 1.037 0.008 1.021 1.052 4.735 0.000 *** Gendermale 0.042 1.043 1.007 0.145 7.512 0.042 0.966 RaceBlack -0.015 0.985 0.622 0.291 3.333 -0.024 0.981 RaceWhite -0.233 0.792 0.597 0.246 2.553 -0.390 0.697 Stage2 0.409 1.506 0.304 0.830 2.731 1.348 0.178 Stage3 1.188 3.280 0.313 1.777 6.056 3.798 0.000 *** Stage4 2.500 12.186 0.392 5.647 26.297 6.371 0.000 *** Purity 0.499 1.648 0.425 0.717 3.788 1.176 0.240 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.41e-12 Wald test p = 6.18e-16 Score (logrank) test p = 7.33e-22 FDPS in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.185 8.310000e-01 0.387 0.389 1.775 -0.478 0.632 Age 0.011 1.011000e+00 0.018 0.976 1.046 0.607 0.544 RaceBlack -0.826 4.380000e-01 1.121 0.049 3.941 -0.737 0.461 RaceWhite -1.194 3.030000e-01 1.118 0.034 2.709 -1.068 0.285 Stage2 18.646 1.252942e+08 6499.351 0.000 Inf 0.003 0.998 Stage3 20.059 5.144197e+08 6499.351 0.000 Inf 0.003 0.998 Stage4 21.329 1.833233e+09 6499.351 0.000 Inf 0.003 0.997 Purity 0.762 2.142000e+00 0.961 0.326 14.083 0.793 0.428 Rsquare = 0.158 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.76e-04 Wald test p = 7.6e-03 Score (logrank) test p = 4.55e-06 FDPS in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.569 1.767000e+00 0.509 0.651 4.792 1.118 0.264 Age 0.029 1.029000e+00 0.028 0.974 1.088 1.014 0.311 RaceBlack -3.048 4.700000e-02 1.850 0.001 1.781 -1.648 0.099 · RaceWhite -1.502 2.230000e-01 1.473 0.012 3.992 -1.020 0.308 Stage2 17.116 2.713625e+07 9860.573 0.000 Inf 0.002 0.999 Stage3 18.873 1.572451e+08 9860.573 0.000 Inf 0.002 0.998 Stage4 51.914 3.516700e+22 2832446.755 0.000 Inf 0.000 1.000 Purity 2.745 1.555800e+01 2.413 0.137 1761.670 1.137 0.255 Rsquare = 0.383 (max possible = 6.68e-01 ) Likelihood ratio test p = 3.88e-04 Wald test p = 1e+00 Score (logrank) test p = 2.62e-14 FDPS in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.448 0.639 0.334 0.332 1.228 -1.344 0.179 Age 0.046 1.048 0.012 1.023 1.072 3.897 0.000 *** Gendermale -15.178 0.000 3444.534 0.000 Inf -0.004 0.996 RaceBlack -0.352 0.704 1.177 0.070 7.069 -0.299 0.765 RaceWhite 0.389 1.476 1.037 0.193 11.268 0.375 0.707 Stage2 0.300 1.350 0.375 0.647 2.816 0.800 0.424 Stage3 0.849 2.338 0.394 1.081 5.056 2.157 0.031 * Stage4 2.169 8.748 0.593 2.739 27.943 3.660 0.000 *** Purity 0.417 1.517 0.614 0.456 5.051 0.679 0.497 Rsquare = 0.073 (max possible = 6.81e-01 ) Likelihood ratio test p = 5.03e-05 Wald test p = 1.12e-05 Score (logrank) test p = 2.55e-07 FDPS in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.455 0.634 0.418 0.280 1.439 -1.089 0.276 Age 0.048 1.050 0.020 1.008 1.093 2.367 0.018 * Gendermale 0.862 2.368 1.108 0.270 20.767 0.778 0.437 RaceBlack 16.633 16728971.456 6327.644 0.000 Inf 0.003 0.998 RaceWhite 15.969 8615438.610 6327.644 0.000 Inf 0.003 0.998 Stage2 0.714 2.041 1.067 0.252 16.538 0.669 0.504 Stage3 1.769 5.865 1.073 0.716 48.042 1.648 0.099 · Stage4 2.403 11.060 1.207 1.039 117.743 1.992 0.046 * Purity 0.992 2.696 1.291 0.215 33.846 0.768 0.442 Rsquare = 0.112 (max possible = 6.98e-01 ) Likelihood ratio test p = 2.84e-02 Wald test p = 5.67e-02 Score (logrank) test p = 1.89e-02 FDPS in CESC (n=306): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.547 1.729 0.256 1.047 2.855 2.139 0.032 * Age 0.011 1.011 0.010 0.992 1.031 1.139 0.255 RaceBlack 1.224 3.401 1.071 0.416 27.768 1.142 0.253 RaceWhite 1.130 3.097 1.025 0.415 23.106 1.103 0.270 Purity 0.327 1.386 0.738 0.326 5.893 0.442 0.658 Rsquare = 0.033 (max possible = 8.91e-01 ) Likelihood ratio test p = 1.73e-01 Wald test p = 1.88e-01 Score (logrank) test p = 1.8e-01 FDPS in CHOL (n=36): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.479 0.619 0.541 0.215 1.787 -0.886 0.376 Age 0.020 1.020 0.022 0.977 1.066 0.916 0.360 Gendermale 0.278 1.321 0.557 0.443 3.936 0.499 0.618 RaceBlack -0.084 0.920 1.505 0.048 17.573 -0.056 0.956 RaceWhite -0.887 0.412 0.903 0.070 2.417 -0.982 0.326 Stage2 0.907 2.478 0.723 0.601 10.210 1.256 0.209 Stage3 -16.053 0.000 7007.430 0.000 Inf -0.002 0.998 Stage4 1.237 3.445 0.803 0.713 16.634 1.540 0.124 Purity 1.972 7.185 1.564 0.335 154.081 1.261 0.207 Rsquare = 0.23 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.01e-01 Wald test p = 6.07e-01 Score (logrank) test p = 4.16e-01 FDPS in COAD (n=458): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.281 0.755 0.256 0.458 1.246 -1.099 0.272 Age 0.025 1.025 0.012 1.002 1.049 2.173 0.030 * Gendermale 0.218 1.244 0.270 0.733 2.112 0.809 0.419 RaceBlack -0.484 0.616 0.824 0.123 3.099 -0.587 0.557 RaceWhite -0.542 0.582 0.777 0.127 2.665 -0.698 0.485 Stage2 0.201 1.223 0.563 0.406 3.684 0.357 0.721 Stage3 0.807 2.241 0.549 0.764 6.573 1.469 0.142 Stage4 1.903 6.705 0.552 2.272 19.790 3.446 0.001 ** Purity -0.202 0.817 0.589 0.258 2.592 -0.343 0.732 Rsquare = 0.113 (max possible = 9.04e-01 ) Likelihood ratio test p = 2.91e-04 Wald test p = 9.87e-05 Score (logrank) test p = 1.34e-05 FDPS in DLBC (n=48): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -1.844 0.158 1.138 0.017 1.472 -1.620 0.105 Age 0.011 1.012 0.046 0.924 1.108 0.247 0.805 Gendermale 0.500 1.649 1.056 0.208 13.052 0.474 0.636 RaceBlack -1.109 0.330 1.908 0.008 13.887 -0.581 0.561 RaceWhite -2.390 0.092 1.395 0.006 1.412 -1.713 0.087 · Purity -2.086 0.124 2.467 0.001 15.631 -0.846 0.398 Rsquare = 0.189 (max possible = 5.58e-01 ) Likelihood ratio test p = 1.98e-01 Wald test p = 4.15e-01 Score (logrank) test p = 1.94e-01 FDPS in ESCA (n=185): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.189 0.828 0.285 0.474 1.446 -0.665 0.506 Age 0.009 1.009 0.014 0.981 1.037 0.625 0.532 Gendermale 0.509 1.663 0.539 0.579 4.779 0.944 0.345 RaceBlack 0.345 1.412 1.068 0.174 11.463 0.323 0.747 RaceWhite -0.101 0.904 0.448 0.376 2.174 -0.225 0.822 Stage2 0.659 1.933 0.656 0.534 6.988 1.005 0.315 Stage3 1.429 4.177 0.671 1.121 15.558 2.130 0.033 * Stage4 2.837 17.060 0.772 3.755 77.515 3.673 0.000 *** Purity 0.331 1.392 0.789 0.297 6.534 0.420 0.675 Rsquare = 0.144 (max possible = 9.32e-01 ) Likelihood ratio test p = 9.82e-03 Wald test p = 4.63e-03 Score (logrank) test p = 3.81e-04 FDPS in GBM (n=153): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.403 0.668 0.310 0.364 1.227 -1.300 0.194 Age 0.030 1.031 0.008 1.014 1.048 3.662 0.000 *** Gendermale -0.036 0.965 0.218 0.630 1.479 -0.163 0.870 RaceBlack 0.616 1.852 0.728 0.445 7.716 0.847 0.397 RaceWhite -0.250 0.779 0.614 0.234 2.598 -0.406 0.685 Purity -0.825 0.438 0.580 0.141 1.365 -1.423 0.155 Rsquare = 0.14 (max possible = 9.98e-01 ) Likelihood ratio test p = 2.36e-03 Wald test p = 4.1e-03 Score (logrank) test p = 3.51e-03 FDPS in HNSC (n=522): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.024 1.024 0.137 0.784 1.338 0.174 0.862 Age 0.022 1.022 0.008 1.007 1.038 2.846 0.004 ** Gendermale -0.251 0.778 0.172 0.555 1.090 -1.459 0.145 RaceBlack 0.124 1.132 0.562 0.376 3.404 0.220 0.826 RaceWhite -0.250 0.778 0.511 0.286 2.120 -0.490 0.624 Stage2 0.610 1.840 0.545 0.632 5.355 1.119 0.263 Stage3 0.846 2.330 0.537 0.813 6.679 1.575 0.115 Stage4 1.249 3.486 0.511 1.281 9.490 2.444 0.015 * Purity -0.060 0.942 0.372 0.454 1.954 -0.161 0.872 Rsquare = 0.069 (max possible = 9.89e-01 ) Likelihood ratio test p = 5.21e-04 Wald test p = 1.39e-03 Score (logrank) test p = 1.02e-03 FDPS in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.202 8.170000e-01 0.473 0.323 2.065 -0.427 0.669 Age 0.011 1.011000e+00 0.025 0.962 1.062 0.419 0.675 Gendermale -0.156 8.550000e-01 0.540 0.297 2.463 -0.289 0.772 RaceBlack 18.987 1.761001e+08 12052.158 0.000 Inf 0.002 0.999 RaceWhite 18.191 7.947473e+07 12052.158 0.000 Inf 0.002 0.999 Stage2 17.485 3.923881e+07 5249.959 0.000 Inf 0.003 0.997 Stage3 16.599 1.618123e+07 5249.959 0.000 Inf 0.003 0.997 Stage4 17.491 3.947142e+07 5249.959 0.000 Inf 0.003 0.997 Purity -1.532 2.160000e-01 1.067 0.027 1.752 -1.435 0.151 Rsquare = 0.09 (max possible = 9.17e-01 ) Likelihood ratio test p = 7.29e-01 Wald test p = 9.47e-01 Score (logrank) test p = 8.6e-01 FDPS in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.023 1.023 0.144 0.771 1.358 0.161 0.872 Age 0.027 1.027 0.009 1.010 1.044 3.102 0.002 ** Gendermale -0.288 0.750 0.183 0.524 1.074 -1.568 0.117 RaceBlack -0.028 0.973 0.568 0.320 2.959 -0.049 0.961 RaceWhite -0.398 0.672 0.512 0.246 1.834 -0.777 0.437 Stage2 0.359 1.433 0.555 0.482 4.255 0.647 0.518 Stage3 0.721 2.057 0.542 0.711 5.950 1.331 0.183 Stage4 1.141 3.130 0.513 1.145 8.561 2.223 0.026 * Purity 0.196 1.217 0.409 0.545 2.715 0.479 0.632 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.37e-04 Wald test p = 9.56e-04 Score (logrank) test p = 7.2e-04 FDPS in KICH (n=66): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p FDPS 1.716 5.565000e+00 0.721 1.355 2.286100e+01 2.381 0.017 Age 0.083 1.087000e+00 0.028 1.028 1.149000e+00 2.945 0.003 Gendermale -1.573 2.080000e-01 0.727 0.050 8.630000e-01 -2.162 0.031 RaceBlack -15.519 0.000000e+00 5120.474 0.000 Inf -0.003 0.998 RaceWhite -0.790 4.540000e-01 1.157 0.047 4.388000e+00 -0.682 0.495 Stage2 15.127 3.710908e+06 0.846 706761.279 1.948443e+07 17.878 0.000 Stage3 16.690 1.771882e+07 0.787 3791499.320 8.280539e+07 21.216 0.000 Stage4 18.481 1.061881e+08 0.897 18314410.151 6.156856e+08 20.609 0.000 Purity 1.306 3.692000e+00 3.862 0.002 7.156648e+03 0.338 0.735 signif FDPS * Age ** Gendermale * RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.37 (max possible = 6.71e-01 ) Likelihood ratio test p = 6.2e-04 Wald test p = 1.1e-255 Score (logrank) test p = 6.45e-09 FDPS in KIRC (n=533): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.485 0.616 0.196 0.419 0.904 -2.471 0.013 * Age 0.033 1.033 0.008 1.016 1.051 3.874 0.000 *** Gendermale -0.164 0.849 0.188 0.587 1.227 -0.871 0.384 RaceBlack 0.403 1.496 1.060 0.187 11.949 0.380 0.704 RaceWhite 0.430 1.538 1.022 0.208 11.393 0.421 0.674 Stage2 0.237 1.268 0.345 0.645 2.492 0.689 0.491 Stage3 0.686 1.986 0.237 1.249 3.158 2.898 0.004 ** Stage4 1.641 5.160 0.221 3.348 7.951 7.437 0.000 *** Purity -0.086 0.917 0.364 0.450 1.871 -0.238 0.812 Rsquare = 0.185 (max possible = 9.65e-01 ) Likelihood ratio test p = 7.02e-16 Wald test p = 6.73e-16 Score (logrank) test p = 2.9e-19 FDPS in KIRP (n=290): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.769 2.158 0.506 0.800 5.822 1.518 0.129 Age 0.007 1.007 0.016 0.977 1.039 0.475 0.635 Gendermale -0.456 0.634 0.384 0.299 1.345 -1.188 0.235 RaceBlack -2.154 0.116 1.208 0.011 1.237 -1.784 0.074 · RaceWhite -2.177 0.113 1.192 0.011 1.172 -1.827 0.068 · Stage2 -0.385 0.681 1.056 0.086 5.388 -0.364 0.716 Stage3 1.565 4.782 0.429 2.063 11.081 3.649 0.000 *** Stage4 2.836 17.054 0.521 6.142 47.348 5.444 0.000 *** Purity -0.454 0.635 0.756 0.144 2.791 -0.601 0.548 Rsquare = 0.172 (max possible = 7.58e-01 ) Likelihood ratio test p = 4.15e-06 Wald test p = 2.09e-06 Score (logrank) test p = 3.28e-10 FDPS in LAML (n=173): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 1.287 3.624 0.399 1.657 7.926 3.224 0.001 ** Age 0.042 1.043 0.008 1.026 1.059 5.133 0.000 *** Gendermale -0.189 0.827 0.212 0.546 1.254 -0.893 0.372 RaceBlack -0.536 0.585 1.107 0.067 5.122 -0.484 0.628 RaceWhite -0.946 0.388 1.022 0.052 2.880 -0.925 0.355 Rsquare = 0.217 (max possible = 9.96e-01 ) Likelihood ratio test p = 7.82e-07 Wald test p = 5.26e-06 Score (logrank) test p = 4.62e-06 FDPS in LGG (n=516): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.699 0.497 0.221 0.322 0.767 -3.157 0.002 ** Age 0.063 1.065 0.008 1.049 1.081 8.239 0.000 *** Gendermale -0.054 0.947 0.200 0.639 1.403 -0.272 0.786 RaceBlack 15.096 3598932.727 2247.325 0.000 Inf 0.007 0.995 RaceWhite 15.246 4178844.527 2247.325 0.000 Inf 0.007 0.995 Purity -0.604 0.546 0.428 0.236 1.265 -1.412 0.158 Rsquare = 0.156 (max possible = 9.07e-01 ) Likelihood ratio test p = 7.6e-15 Wald test p = 3.33e-15 Score (logrank) test p = 2.76e-16 FDPS in LIHC (n=371): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.175 1.191 0.110 0.960 1.478 1.591 0.112 Age 0.010 1.010 0.008 0.994 1.026 1.187 0.235 Gendermale -0.114 0.892 0.227 0.572 1.392 -0.503 0.615 RaceBlack 0.830 2.294 0.493 0.873 6.024 1.685 0.092 · RaceWhite 0.031 1.031 0.237 0.648 1.642 0.129 0.897 Stage2 0.338 1.402 0.261 0.841 2.339 1.296 0.195 Stage3 0.912 2.490 0.236 1.569 3.951 3.871 0.000 *** Stage4 1.489 4.434 0.621 1.313 14.978 2.398 0.016 * Purity 0.442 1.555 0.467 0.623 3.881 0.947 0.344 Rsquare = 0.092 (max possible = 9.66e-01 ) Likelihood ratio test p = 4.45e-04 Wald test p = 2.68e-04 Score (logrank) test p = 1e-04 FDPS in LUAD (n=515): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.482 1.619 0.150 1.206 2.172 3.208 0.001 ** Age 0.007 1.007 0.009 0.989 1.025 0.776 0.438 Gendermale 0.023 1.023 0.168 0.735 1.423 0.135 0.892 RaceBlack 16.267 11605975.749 1860.546 0.000 Inf 0.009 0.993 RaceWhite 16.423 13569383.955 1860.546 0.000 Inf 0.009 0.993 Stage2 0.859 2.362 0.201 1.593 3.500 4.280 0.000 *** Stage3 1.013 2.755 0.218 1.797 4.222 4.650 0.000 *** Stage4 0.932 2.540 0.332 1.324 4.874 2.804 0.005 ** Purity 0.599 1.820 0.345 0.926 3.577 1.738 0.082 · Rsquare = 0.118 (max possible = 9.74e-01 ) Likelihood ratio test p = 3.33e-08 Wald test p = 7.43e-07 Score (logrank) test p = 8.19e-08 FDPS in LUSC (n=501): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.102 0.903 0.136 0.692 1.178 -0.753 0.451 Age 0.016 1.016 0.009 0.997 1.035 1.678 0.093 · Gendermale 0.442 1.555 0.194 1.064 2.275 2.278 0.023 * RaceBlack -0.001 0.999 0.606 0.305 3.280 -0.001 0.999 RaceWhite -0.514 0.598 0.563 0.198 1.804 -0.912 0.362 Stage2 0.215 1.240 0.187 0.860 1.788 1.155 0.248 Stage3 0.595 1.813 0.215 1.190 2.760 2.772 0.006 ** Stage4 0.774 2.168 0.795 0.457 10.292 0.974 0.330 Purity -0.320 0.726 0.367 0.353 1.491 -0.872 0.383 Rsquare = 0.052 (max possible = 9.87e-01 ) Likelihood ratio test p = 2e-02 Wald test p = 1.54e-02 Score (logrank) test p = 1.33e-02 FDPS in MESO (n=87): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 1.065 2.900 0.261 1.740 4.833 4.085 0.000 *** Age 0.009 1.009 0.016 0.977 1.041 0.524 0.600 Gendermale -0.273 0.761 0.336 0.394 1.470 -0.812 0.417 RaceBlack -0.435 0.648 1.535 0.032 13.118 -0.283 0.777 RaceWhite -0.221 0.802 1.047 0.103 6.240 -0.211 0.833 Stage2 -0.271 0.763 0.465 0.307 1.897 -0.583 0.560 Stage3 -0.349 0.705 0.425 0.306 1.624 -0.820 0.412 Stage4 -0.370 0.691 0.476 0.271 1.757 -0.777 0.437 Purity -0.525 0.591 0.601 0.182 1.922 -0.874 0.382 Rsquare = 0.215 (max possible = 9.98e-01 ) Likelihood ratio test p = 1.44e-02 Wald test p = 8.05e-03 Score (logrank) test p = 8.38e-03 FDPS in OV (n=303): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.414 0.661 0.179 0.465 0.940 -2.305 0.021 * Age 0.036 1.036 0.008 1.020 1.053 4.381 0.000 *** RaceBlack -0.198 0.821 0.581 0.263 2.561 -0.341 0.733 RaceWhite -0.313 0.732 0.519 0.264 2.025 -0.602 0.547 Purity -0.299 0.742 0.667 0.201 2.741 -0.448 0.654 Rsquare = 0.102 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.09e-04 Wald test p = 1.04e-04 Score (logrank) test p = 8.48e-05 FDPS in PAAD (n=179): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.015 1.015 0.175 0.720 1.430 0.085 0.932 Age 0.022 1.022 0.011 1.000 1.044 1.980 0.048 * Gendermale -0.215 0.806 0.217 0.528 1.233 -0.994 0.320 RaceBlack -0.023 0.977 0.738 0.230 4.149 -0.031 0.975 RaceWhite 0.361 1.434 0.474 0.566 3.631 0.761 0.447 Stage2 0.621 1.861 0.438 0.788 4.393 1.417 0.156 Stage3 -0.235 0.791 1.092 0.093 6.721 -0.215 0.830 Stage4 0.225 1.252 0.836 0.243 6.444 0.269 0.788 Purity -0.669 0.512 0.409 0.230 1.143 -1.634 0.102 Rsquare = 0.089 (max possible = 9.91e-01 ) Likelihood ratio test p = 8.32e-02 Wald test p = 1.2e-01 Score (logrank) test p = 1.15e-01 FDPS in PCPG (n=181): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.933 2.542 0.591 0.799 8.089 1.580 0.114 Age 0.046 1.047 0.031 0.985 1.112 1.479 0.139 Gendermale 1.488 4.427 0.938 0.704 27.834 1.586 0.113 RaceBlack -0.725 0.484 19390.598 0.000 Inf 0.000 1.000 RaceWhite 16.811 19997976.479 16243.620 0.000 Inf 0.001 0.999 Purity 6.661 781.608 3.396 1.006 607276.952 1.962 0.050 · Rsquare = 0.066 (max possible = 3.07e-01 ) Likelihood ratio test p = 8.06e-02 Wald test p = 2.67e-01 Score (logrank) test p = 2.09e-01 FDPS in PRAD (n=498): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.773 2.165 0.492 0.825 5.683 1.569 0.117 Age 0.012 1.012 0.058 0.904 1.133 0.210 0.834 RaceBlack 16.259 11516131.046 10821.951 0.000 Inf 0.002 0.999 RaceWhite 17.397 35927203.356 10821.951 0.000 Inf 0.002 0.999 Purity 1.015 2.760 1.398 0.178 42.753 0.726 0.468 Rsquare = 0.012 (max possible = 1.83e-01 ) Likelihood ratio test p = 4.5e-01 Wald test p = 4.44e-01 Score (logrank) test p = 4.21e-01 FDPS in READ (n=166): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.949 2.582 0.642 0.733 9.095 1.476 0.140 Age 0.135 1.145 0.049 1.039 1.261 2.733 0.006 ** Gendermale -0.575 0.563 0.711 0.140 2.267 -0.809 0.419 RaceBlack 12.217 202145.489 10625.299 0.000 Inf 0.001 0.999 RaceWhite 10.807 49382.530 10625.299 0.000 Inf 0.001 0.999 Stage2 -1.696 0.183 1.284 0.015 2.271 -1.321 0.186 Stage3 -0.012 0.988 0.948 0.154 6.330 -0.013 0.990 Stage4 0.161 1.175 0.972 0.175 7.905 0.166 0.868 Purity 0.662 1.938 1.468 0.109 34.411 0.451 0.652 Rsquare = 0.232 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.76e-02 Wald test p = 2.11e-01 Score (logrank) test p = 3.51e-02 FDPS in SARC (n=260): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.192 1.212 0.195 0.828 1.774 0.987 0.324 Age 0.022 1.022 0.008 1.006 1.039 2.656 0.008 ** Gendermale 0.025 1.025 0.225 0.659 1.594 0.110 0.912 RaceBlack -0.203 0.817 1.089 0.097 6.901 -0.186 0.852 RaceWhite -0.549 0.577 1.026 0.077 4.317 -0.535 0.593 Purity 0.934 2.544 0.570 0.832 7.774 1.638 0.101 Rsquare = 0.046 (max possible = 9.75e-01 ) Likelihood ratio test p = 8.64e-02 Wald test p = 1.23e-01 Score (logrank) test p = 1.24e-01 FDPS in SKCM (n=471): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.198 1.219 0.126 0.952 1.559 1.572 0.116 Age 0.018 1.018 0.005 1.008 1.028 3.442 0.001 ** Gendermale -0.073 0.929 0.158 0.682 1.267 -0.464 0.643 RaceWhite -1.352 0.259 0.404 0.117 0.571 -3.345 0.001 ** Stage2 0.264 1.302 0.218 0.849 1.996 1.209 0.227 Stage3 0.614 1.848 0.204 1.240 2.755 3.015 0.003 ** Stage4 1.326 3.766 0.352 1.891 7.502 3.772 0.000 *** Purity 0.990 2.691 0.338 1.387 5.222 2.926 0.003 ** Rsquare = 0.129 (max possible = 9.92e-01 ) Likelihood ratio test p = 7.72e-09 Wald test p = 4.04e-09 Score (logrank) test p = 4.57e-10 FDPS in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.299 1.348000e+00 0.346 0.685 2.654 0.864 0.388 Age 0.012 1.012000e+00 0.016 0.980 1.044 0.721 0.471 Gendermale 0.269 1.309000e+00 0.441 0.551 3.108 0.611 0.541 RaceWhite -1.244 2.880000e-01 0.632 0.084 0.994 -1.970 0.049 * Stage2 17.456 3.812081e+07 6232.125 0.000 Inf 0.003 0.998 Stage3 18.028 6.753642e+07 6232.125 0.000 Inf 0.003 0.998 Stage4 20.111 5.422346e+08 6232.125 0.000 Inf 0.003 0.997 Purity 0.061 1.063000e+00 0.971 0.159 7.124 0.063 0.950 Rsquare = 0.153 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.76e-02 Wald test p = 4.41e-02 Score (logrank) test p = 3.38e-03 FDPS in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.205 1.228 0.137 0.939 1.605 1.501 0.133 Age 0.020 1.020 0.006 1.009 1.031 3.546 0.000 *** Gendermale -0.087 0.917 0.173 0.653 1.287 -0.502 0.615 RaceWhite -1.169 0.311 0.605 0.095 1.017 -1.932 0.053 · Stage2 0.139 1.149 0.230 0.732 1.804 0.603 0.546 Stage3 0.562 1.755 0.209 1.166 2.641 2.695 0.007 ** Stage4 1.110 3.035 0.399 1.388 6.639 2.780 0.005 ** Purity 1.115 3.051 0.368 1.484 6.273 3.032 0.002 ** Rsquare = 0.14 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.3e-07 Wald test p = 6.14e-07 Score (logrank) test p = 2.37e-07 FDPS in STAD (n=415): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.123 0.884 0.184 0.617 1.267 -0.673 0.501 Age 0.027 1.027 0.010 1.007 1.048 2.641 0.008 ** Gendermale 0.133 1.142 0.208 0.759 1.717 0.638 0.524 RaceBlack 0.250 1.285 0.448 0.534 3.090 0.559 0.576 RaceWhite 0.090 1.094 0.244 0.677 1.766 0.367 0.714 Stage2 0.488 1.630 0.390 0.759 3.497 1.253 0.210 Stage3 0.922 2.513 0.363 1.234 5.121 2.538 0.011 * Stage4 1.325 3.762 0.503 1.402 10.093 2.632 0.008 ** Purity -0.539 0.583 0.380 0.277 1.229 -1.417 0.156 Rsquare = 0.071 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.17e-02 Wald test p = 1.61e-02 Score (logrank) test p = 1.3e-02 FDPS in TGCT (n=150): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 2.981 19.700 20869.190 0 Inf 0.000 1.000 Age -1.578 0.206 1807.111 0 Inf -0.001 0.999 RaceBlack 12.374 236501.893 17780837.962 0 Inf 0.000 1.000 RaceWhite -29.758 0.000 17394776.637 0 Inf 0.000 1.000 Stage2 -2.824 0.059 42821.081 0 Inf 0.000 1.000 Stage3 15.237 4142615.245 101794.528 0 Inf 0.000 1.000 Purity 4.445 85.167 230392.617 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 2.9e-03 FDPS in THCA (n=509): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.584 0.558 0.872 0.101 3.079 -0.670 0.503 Age 0.146 1.157 0.028 1.096 1.222 5.268 0.000 *** Gendermale -0.074 0.929 0.624 0.273 3.157 -0.118 0.906 RaceBlack 16.801 19803739.988 6212.266 0.000 Inf 0.003 0.998 RaceWhite 16.519 14929915.927 6212.266 0.000 Inf 0.003 0.998 Stage2 -0.302 0.739 1.095 0.086 6.324 -0.276 0.783 Stage3 0.276 1.318 0.849 0.250 6.957 0.325 0.745 Stage4 1.670 5.311 0.982 0.775 36.377 1.701 0.089 · Purity 2.327 10.248 1.147 1.083 96.966 2.030 0.042 * Rsquare = 0.15 (max possible = 3.47e-01 ) Likelihood ratio test p = 2.08e-10 Wald test p = 3.3e-04 Score (logrank) test p = 8.96e-11 FDPS in THYM (n=120): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.616 0.540 1.089 0.064 4.561 -0.566 0.572 Age 0.042 1.043 0.033 0.978 1.113 1.272 0.203 Gendermale -0.234 0.791 0.738 0.186 3.361 -0.318 0.751 RaceBlack -16.331 0.000 10103.860 0.000 Inf -0.002 0.999 RaceWhite 0.633 1.883 1.127 0.207 17.144 0.561 0.575 Purity 0.430 1.537 1.106 0.176 13.417 0.389 0.698 Rsquare = 0.047 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.91e-01 Wald test p = 6.15e-01 Score (logrank) test p = 5.17e-01 FDPS in UCEC (n=545): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 0.153 1.166 0.231 0.741 1.833 0.663 0.507 Age 0.048 1.049 0.016 1.017 1.083 2.986 0.003 ** RaceBlack -0.391 0.677 0.795 0.143 3.211 -0.492 0.623 RaceWhite -0.494 0.610 0.747 0.141 2.636 -0.662 0.508 Purity 0.407 1.502 0.646 0.424 5.324 0.630 0.529 Rsquare = 0.04 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.24e-02 Wald test p = 5.05e-02 Score (logrank) test p = 4.83e-02 FDPS in UCS (n=57): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS -0.095 0.910 0.329 0.477 1.734 -0.288 0.774 Age 0.043 1.044 0.024 0.996 1.095 1.785 0.074 · RaceBlack 17.615 44700177.489 6480.492 0.000 Inf 0.003 0.998 RaceWhite 17.822 54963111.305 6480.492 0.000 Inf 0.003 0.998 Purity -0.914 0.401 1.073 0.049 3.282 -0.852 0.394 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.47e-01 Wald test p = 3.45e-01 Score (logrank) test p = 2.54e-01 FDPS in UVM (n=80): Model: Surv(OS, EVENT) ~ `FDPS` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif FDPS 1.853 6.377 0.625 1.873 21.716 2.964 0.003 ** Age 0.042 1.043 0.019 1.005 1.082 2.233 0.026 * Gendermale 0.197 1.218 0.485 0.471 3.148 0.407 0.684 Stage3 0.159 1.172 0.518 0.424 3.237 0.307 0.759 Stage4 3.721 41.294 1.203 3.910 436.124 3.094 0.002 ** Purity 1.683 5.379 1.327 0.400 72.428 1.268 0.205 Rsquare = 0.346 (max possible = 8.72e-01 ) Likelihood ratio test p = 1.19e-05 Wald test p = 4.76e-04 Score (logrank) test p = 1.15e-10 HMGCR in ACC (n=79): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.163 1.178 0.162 0.856 1.619 1.006 0.315 Age 0.007 1.008 0.014 0.980 1.036 0.527 0.598 Gendermale 0.435 1.545 0.423 0.675 3.536 1.030 0.303 RaceBlack -0.556 0.574 12098.108 0.000 Inf 0.000 1.000 RaceWhite 16.407 13349012.441 10314.788 0.000 Inf 0.002 0.999 Purity 2.112 8.261 2.400 0.075 912.164 0.880 0.379 Rsquare = 0.081 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.9e-01 Wald test p = 7.93e-01 Score (logrank) test p = 6.29e-01 HMGCR in BLCA (n=408): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.201 1.223 0.137 0.936 1.599 1.474 0.140 Age 0.032 1.032 0.009 1.015 1.050 3.707 0.000 *** Gendermale -0.191 0.826 0.179 0.581 1.174 -1.067 0.286 RaceBlack 0.607 1.835 0.453 0.755 4.460 1.339 0.181 RaceWhite 0.086 1.090 0.355 0.543 2.186 0.241 0.809 Stage2 14.487 1957986.153 1885.842 0.000 Inf 0.008 0.994 Stage3 14.965 3156815.429 1885.842 0.000 Inf 0.008 0.994 Stage4 15.478 5270541.001 1885.842 0.000 Inf 0.008 0.993 Purity -0.022 0.978 0.357 0.486 1.969 -0.062 0.951 Rsquare = 0.136 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.93e-08 Wald test p = 4.02e-07 Score (logrank) test p = 1.13e-07 HMGCR in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.224 1.251 0.150 0.931 1.680 1.488 0.137 Age 0.036 1.037 0.008 1.021 1.052 4.740 0.000 *** Gendermale -0.006 0.994 1.008 0.138 7.164 -0.006 0.995 RaceBlack 0.022 1.022 0.619 0.304 3.440 0.035 0.972 RaceWhite -0.267 0.766 0.597 0.238 2.467 -0.447 0.655 Stage2 0.413 1.512 0.304 0.834 2.742 1.361 0.173 Stage3 1.180 3.253 0.313 1.762 6.007 3.769 0.000 *** Stage4 2.582 13.218 0.391 6.137 28.471 6.594 0.000 *** Purity 0.416 1.516 0.427 0.657 3.500 0.975 0.330 Rsquare = 0.083 (max possible = 7.85e-01 ) Likelihood ratio test p = 9.16e-13 Wald test p = 3.17e-16 Score (logrank) test p = 3.52e-22 HMGCR in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.082 9.210000e-01 0.337 0.476 1.783 -0.244 0.807 Age 0.011 1.011000e+00 0.018 0.977 1.046 0.625 0.532 RaceBlack -0.922 3.980000e-01 1.110 0.045 3.501 -0.831 0.406 RaceWhite -1.234 2.910000e-01 1.120 0.032 2.615 -1.102 0.271 Stage2 18.680 1.296466e+08 6489.158 0.000 Inf 0.003 0.998 Stage3 20.109 5.408630e+08 6489.158 0.000 Inf 0.003 0.998 Stage4 21.388 1.944446e+09 6489.158 0.000 Inf 0.003 0.997 Purity 0.763 2.145000e+00 0.957 0.329 13.988 0.797 0.425 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.09e-04 Wald test p = 7.03e-03 Score (logrank) test p = 4.59e-06 HMGCR in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.575 1.777000e+00 0.803 0.368 8.579 0.716 0.474 Age 0.026 1.027000e+00 0.029 0.970 1.086 0.912 0.362 RaceBlack -2.688 6.800000e-02 1.867 0.002 2.643 -1.439 0.150 RaceWhite -1.357 2.570000e-01 1.536 0.013 5.230 -0.883 0.377 Stage2 18.381 9.612166e+07 14457.207 0.000 Inf 0.001 0.999 Stage3 19.994 4.821867e+08 14457.207 0.000 Inf 0.001 0.999 Stage4 52.354 5.459933e+22 1936714.287 0.000 Inf 0.000 1.000 Purity 3.320 2.766800e+01 2.436 0.234 3278.467 1.363 0.173 Rsquare = 0.376 (max possible = 6.68e-01 ) Likelihood ratio test p = 5.14e-04 Wald test p = 1e+00 Score (logrank) test p = 3.94e-14 HMGCR in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.208 1.231 0.238 0.772 1.963 0.873 0.383 Age 0.049 1.050 0.012 1.026 1.075 4.101 0.000 *** Gendermale -15.408 0.000 3465.429 0.000 Inf -0.004 0.996 RaceBlack -0.447 0.640 1.174 0.064 6.386 -0.380 0.704 RaceWhite 0.147 1.159 1.038 0.151 8.865 0.142 0.887 Stage2 0.322 1.380 0.374 0.663 2.872 0.861 0.389 Stage3 0.896 2.449 0.395 1.129 5.315 2.266 0.023 * Stage4 2.194 8.971 0.594 2.800 28.739 3.693 0.000 *** Purity 0.245 1.278 0.619 0.380 4.300 0.396 0.692 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 7.69e-05 Wald test p = 1.61e-05 Score (logrank) test p = 2.92e-07 HMGCR in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.042 1.043 0.393 0.483 2.252 0.108 0.914 Age 0.050 1.051 0.021 1.009 1.095 2.398 0.016 * Gendermale 0.970 2.637 1.108 0.301 23.126 0.875 0.381 RaceBlack 16.591 16041257.715 6512.845 0.000 Inf 0.003 0.998 RaceWhite 15.964 8569881.425 6512.845 0.000 Inf 0.002 0.998 Stage2 0.666 1.946 1.082 0.233 16.236 0.615 0.538 Stage3 1.579 4.851 1.078 0.587 40.092 1.466 0.143 Stage4 2.101 8.175 1.176 0.815 81.960 1.786 0.074 · Purity 1.003 2.727 1.358 0.191 39.037 0.739 0.460 Rsquare = 0.105 (max possible = 6.98e-01 ) Likelihood ratio test p = 4.19e-02 Wald test p = 7.1e-02 Score (logrank) test p = 2.42e-02 HMGCR in CESC (n=306): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.055 0.947 0.212 0.625 1.434 -0.259 0.795 Age 0.012 1.012 0.010 0.992 1.032 1.162 0.245 RaceBlack 1.055 2.873 1.069 0.354 23.342 0.987 0.323 RaceWhite 0.828 2.288 1.015 0.313 16.732 0.815 0.415 Purity 0.596 1.814 0.740 0.426 7.734 0.805 0.421 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.69e-01 Wald test p = 7.08e-01 Score (logrank) test p = 6.99e-01 HMGCR in CHOL (n=36): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -1.350 0.259 0.823 0.052 1.301 -1.641 0.101 Age 0.018 1.019 0.024 0.971 1.068 0.761 0.447 Gendermale 0.357 1.429 0.567 0.470 4.345 0.629 0.529 RaceBlack 0.097 1.101 1.563 0.052 23.556 0.062 0.951 RaceWhite -0.561 0.570 0.994 0.081 3.998 -0.565 0.572 Stage2 1.024 2.784 0.715 0.686 11.297 1.433 0.152 Stage3 -16.775 0.000 7132.490 0.000 Inf -0.002 0.998 Stage4 0.988 2.686 0.656 0.743 9.709 1.507 0.132 Purity 1.156 3.176 1.705 0.112 89.751 0.678 0.498 Rsquare = 0.275 (max possible = 9.46e-01 ) Likelihood ratio test p = 2.38e-01 Wald test p = 5.07e-01 Score (logrank) test p = 2.87e-01 HMGCR in COAD (n=458): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.199 0.819 0.186 0.569 1.179 -1.073 0.283 Age 0.024 1.024 0.011 1.002 1.048 2.101 0.036 * Gendermale 0.203 1.226 0.271 0.720 2.085 0.750 0.453 RaceBlack -0.476 0.621 0.829 0.122 3.152 -0.574 0.566 RaceWhite -0.558 0.572 0.783 0.123 2.656 -0.713 0.476 Stage2 0.210 1.234 0.562 0.410 3.713 0.373 0.709 Stage3 0.785 2.193 0.549 0.747 6.438 1.430 0.153 Stage4 1.885 6.589 0.552 2.235 19.429 3.418 0.001 ** Purity -0.279 0.756 0.594 0.236 2.421 -0.470 0.638 Rsquare = 0.113 (max possible = 9.04e-01 ) Likelihood ratio test p = 2.99e-04 Wald test p = 9.33e-05 Score (logrank) test p = 1.43e-05 HMGCR in DLBC (n=48): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 3.835 46.311 1.862 1.204 1780.626 2.060 0.039 * Age -0.026 0.974 0.046 0.891 1.066 -0.563 0.573 Gendermale 0.621 1.861 1.086 0.221 15.648 0.572 0.567 RaceBlack 3.597 36.487 2.705 0.182 7325.400 1.330 0.184 RaceWhite -2.933 0.053 1.548 0.003 1.107 -1.894 0.058 · Purity -1.485 0.227 1.847 0.006 8.457 -0.804 0.421 Rsquare = 0.261 (max possible = 5.58e-01 ) Likelihood ratio test p = 5.37e-02 Wald test p = 2.18e-01 Score (logrank) test p = 4.63e-02 HMGCR in ESCA (n=185): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.088 1.092 0.219 0.710 1.678 0.401 0.688 Age 0.010 1.010 0.014 0.983 1.038 0.697 0.486 Gendermale 0.493 1.637 0.539 0.569 4.709 0.914 0.360 RaceBlack 0.309 1.362 1.070 0.167 11.088 0.289 0.773 RaceWhite -0.074 0.929 0.448 0.386 2.234 -0.165 0.869 Stage2 0.706 2.027 0.655 0.562 7.311 1.079 0.281 Stage3 1.470 4.349 0.671 1.167 16.207 2.190 0.029 * Stage4 2.873 17.684 0.775 3.869 80.832 3.705 0.000 *** Purity 0.166 1.180 0.773 0.260 5.366 0.215 0.830 Rsquare = 0.142 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.09e-02 Wald test p = 5.01e-03 Score (logrank) test p = 4.07e-04 HMGCR in GBM (n=153): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.035 0.966 0.199 0.653 1.428 -0.175 0.861 Age 0.029 1.030 0.008 1.013 1.047 3.524 0.000 *** Gendermale -0.097 0.907 0.213 0.597 1.379 -0.455 0.649 RaceBlack 0.531 1.701 0.727 0.409 7.069 0.731 0.465 RaceWhite -0.241 0.786 0.614 0.236 2.619 -0.392 0.695 Purity -1.030 0.357 0.625 0.105 1.215 -1.648 0.099 · Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.75e-03 Wald test p = 6.89e-03 Score (logrank) test p = 6e-03 HMGCR in HNSC (n=522): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.161 1.174 0.123 0.922 1.495 1.304 0.192 Age 0.021 1.022 0.008 1.006 1.037 2.807 0.005 ** Gendermale -0.234 0.792 0.172 0.565 1.110 -1.355 0.175 RaceBlack 0.152 1.165 0.559 0.390 3.481 0.273 0.785 RaceWhite -0.236 0.790 0.511 0.290 2.149 -0.462 0.644 Stage2 0.645 1.905 0.544 0.656 5.535 1.185 0.236 Stage3 0.873 2.394 0.537 0.836 6.855 1.627 0.104 Stage4 1.289 3.629 0.511 1.334 9.871 2.524 0.012 * Purity -0.029 0.972 0.367 0.474 1.994 -0.078 0.938 Rsquare = 0.073 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.7e-04 Wald test p = 7.34e-04 Score (logrank) test p = 5.27e-04 HMGCR in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.480 6.190000e-01 0.385 0.291 1.317 -1.246 0.213 Age 0.017 1.017000e+00 0.026 0.966 1.071 0.639 0.523 Gendermale -0.158 8.540000e-01 0.546 0.293 2.489 -0.290 0.772 RaceBlack 18.818 1.487528e+08 12291.119 0.000 Inf 0.002 0.999 RaceWhite 18.205 8.060661e+07 12291.119 0.000 Inf 0.001 0.999 Stage2 17.494 3.959224e+07 5301.841 0.000 Inf 0.003 0.997 Stage3 16.452 1.396580e+07 5301.841 0.000 Inf 0.003 0.998 Stage4 17.404 3.617200e+07 5301.841 0.000 Inf 0.003 0.997 Purity -1.578 2.060000e-01 1.041 0.027 1.589 -1.515 0.130 Rsquare = 0.109 (max possible = 9.17e-01 ) Likelihood ratio test p = 5.83e-01 Wald test p = 8.58e-01 Score (logrank) test p = 7.26e-01 HMGCR in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.246 1.278 0.134 0.984 1.661 1.839 0.066 · Age 0.026 1.026 0.008 1.010 1.043 3.109 0.002 ** Gendermale -0.258 0.773 0.184 0.538 1.109 -1.399 0.162 RaceBlack 0.010 1.010 0.564 0.334 3.050 0.017 0.986 RaceWhite -0.369 0.692 0.512 0.253 1.887 -0.720 0.471 Stage2 0.396 1.486 0.555 0.501 4.405 0.714 0.475 Stage3 0.737 2.090 0.541 0.724 6.035 1.363 0.173 Stage4 1.193 3.298 0.513 1.208 9.006 2.328 0.020 * Purity 0.273 1.313 0.408 0.591 2.921 0.668 0.504 Rsquare = 0.094 (max possible = 9.89e-01 ) Likelihood ratio test p = 8.81e-05 Wald test p = 2.87e-04 Score (logrank) test p = 2.11e-04 HMGCR in KICH (n=66): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p HMGCR 1.022 2.778 0.682 0.729 1.058100e+01 1.497 0.134 Age 0.084 1.087 0.029 1.027 1.152000e+00 2.869 0.004 Gendermale -0.920 0.398 0.729 0.096 1.662000e+00 -1.263 0.207 RaceBlack -14.900 0.000 3023.288 0.000 Inf -0.005 0.996 RaceWhite -1.246 0.288 1.159 0.030 2.792000e+00 -1.074 0.283 Stage2 14.515 2012602.214 0.851 379692.513 1.066802e+07 17.057 0.000 Stage3 15.529 5550359.834 0.776 1212137.526 2.541502e+07 20.005 0.000 Stage4 17.609 44425479.557 0.902 7578581.483 2.604212e+08 19.516 0.000 Purity 0.863 2.370 3.136 0.005 1.107507e+03 0.275 0.783 signif HMGCR Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.364 (max possible = 6.71e-01 ) Likelihood ratio test p = 7.93e-04 Wald test p = 6.82e-228 Score (logrank) test p = 7.35e-09 HMGCR in KIRC (n=533): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.566 0.568 0.159 0.416 0.775 -3.566 0.000 *** Age 0.034 1.035 0.008 1.018 1.052 4.138 0.000 *** Gendermale -0.076 0.927 0.186 0.644 1.334 -0.407 0.684 RaceBlack 0.409 1.505 1.057 0.189 11.957 0.387 0.699 RaceWhite 0.378 1.460 1.017 0.199 10.713 0.372 0.710 Stage2 0.213 1.237 0.345 0.629 2.434 0.617 0.537 Stage3 0.673 1.961 0.233 1.242 3.095 2.891 0.004 ** Stage4 1.663 5.273 0.218 3.442 8.079 7.639 0.000 *** Purity 0.022 1.022 0.361 0.504 2.075 0.061 0.952 Rsquare = 0.197 (max possible = 9.65e-01 ) Likelihood ratio test p = 2.89e-17 Wald test p = 5.74e-18 Score (logrank) test p = 4e-21 HMGCR in KIRP (n=290): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.141 1.151 0.337 0.594 2.231 0.418 0.676 Age 0.008 1.008 0.016 0.978 1.040 0.517 0.605 Gendermale -0.490 0.613 0.384 0.289 1.300 -1.276 0.202 RaceBlack -1.987 0.137 1.196 0.013 1.429 -1.662 0.097 · RaceWhite -2.025 0.132 1.177 0.013 1.326 -1.720 0.085 · Stage2 -0.406 0.666 1.055 0.084 5.263 -0.385 0.700 Stage3 1.666 5.291 0.433 2.266 12.354 3.851 0.000 *** Stage4 2.714 15.091 0.510 5.559 40.968 5.326 0.000 *** Purity -0.341 0.711 0.762 0.160 3.166 -0.448 0.654 Rsquare = 0.164 (max possible = 7.58e-01 ) Likelihood ratio test p = 1.08e-05 Wald test p = 3.62e-06 Score (logrank) test p = 6.54e-10 HMGCR in LAML (n=173): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.070 1.073 0.225 0.690 1.669 0.311 0.756 Age 0.038 1.039 0.008 1.022 1.055 4.682 0.000 *** Gendermale -0.136 0.873 0.212 0.576 1.322 -0.642 0.521 RaceBlack -0.309 0.734 1.112 0.083 6.499 -0.278 0.781 RaceWhite -0.693 0.500 1.019 0.068 3.683 -0.680 0.496 Rsquare = 0.156 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.22e-04 Wald test p = 3.81e-04 Score (logrank) test p = 2.67e-04 HMGCR in LGG (n=516): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.318 0.727 0.125 0.570 0.928 -2.556 0.011 * Age 0.062 1.064 0.008 1.049 1.081 8.117 0.000 *** Gendermale -0.003 0.997 0.198 0.676 1.469 -0.017 0.986 RaceBlack 15.308 4447510.762 2028.340 0.000 Inf 0.008 0.994 RaceWhite 15.419 4969124.888 2028.340 0.000 Inf 0.008 0.994 Purity -0.701 0.496 0.421 0.217 1.132 -1.665 0.096 · Rsquare = 0.148 (max possible = 9.07e-01 ) Likelihood ratio test p = 5.25e-14 Wald test p = 5.9e-14 Score (logrank) test p = 4.42e-15 HMGCR in LIHC (n=371): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.017 1.017 0.118 0.808 1.281 0.146 0.884 Age 0.011 1.011 0.008 0.995 1.027 1.343 0.179 Gendermale -0.137 0.872 0.226 0.560 1.359 -0.605 0.545 RaceBlack 0.887 2.428 0.490 0.929 6.348 1.809 0.070 · RaceWhite 0.006 1.006 0.238 0.631 1.603 0.023 0.981 Stage2 0.317 1.373 0.262 0.821 2.294 1.208 0.227 Stage3 0.947 2.579 0.235 1.627 4.088 4.032 0.000 *** Stage4 1.597 4.938 0.619 1.466 16.628 2.578 0.010 * Purity 0.566 1.761 0.465 0.708 4.380 1.218 0.223 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.2e-03 Wald test p = 7.14e-04 Score (logrank) test p = 2.71e-04 HMGCR in LUAD (n=515): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.169 1.184 0.137 0.905 1.548 1.232 0.218 Age 0.007 1.007 0.009 0.989 1.025 0.750 0.453 Gendermale -0.002 0.998 0.170 0.716 1.392 -0.009 0.993 RaceBlack 16.161 10435440.521 1898.444 0.000 Inf 0.009 0.993 RaceWhite 16.296 11944488.268 1898.444 0.000 Inf 0.009 0.993 Stage2 0.877 2.403 0.202 1.619 3.567 4.349 0.000 *** Stage3 1.001 2.721 0.218 1.774 4.173 4.587 0.000 *** Stage4 1.008 2.739 0.334 1.424 5.269 3.020 0.003 ** Purity 0.574 1.775 0.343 0.907 3.475 1.674 0.094 · Rsquare = 0.1 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.26e-06 Wald test p = 2e-05 Score (logrank) test p = 2.12e-06 HMGCR in LUSC (n=501): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.179 1.196 0.130 0.927 1.543 1.374 0.170 Age 0.015 1.015 0.009 0.996 1.034 1.583 0.113 Gendermale 0.408 1.504 0.194 1.028 2.202 2.100 0.036 * RaceBlack -0.051 0.951 0.610 0.288 3.141 -0.083 0.934 RaceWhite -0.551 0.576 0.566 0.190 1.747 -0.974 0.330 Stage2 0.179 1.196 0.188 0.826 1.729 0.948 0.343 Stage3 0.628 1.873 0.215 1.228 2.856 2.916 0.004 ** Stage4 0.806 2.238 0.799 0.468 10.708 1.009 0.313 Purity -0.429 0.651 0.371 0.314 1.349 -1.155 0.248 Rsquare = 0.055 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.26e-02 Wald test p = 1.01e-02 Score (logrank) test p = 8.3e-03 HMGCR in MESO (n=87): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.695 2.003 0.227 1.284 3.125 3.059 0.002 ** Age 0.004 1.004 0.017 0.971 1.039 0.234 0.815 Gendermale -0.166 0.847 0.333 0.441 1.628 -0.497 0.619 RaceBlack -0.744 0.475 1.561 0.022 10.136 -0.477 0.634 RaceWhite -0.827 0.437 1.056 0.055 3.463 -0.784 0.433 Stage2 -0.156 0.855 0.462 0.346 2.118 -0.338 0.736 Stage3 -0.144 0.866 0.418 0.381 1.965 -0.345 0.730 Stage4 -0.146 0.864 0.475 0.341 2.193 -0.307 0.759 Purity -0.717 0.488 0.562 0.162 1.469 -1.276 0.202 Rsquare = 0.154 (max possible = 9.98e-01 ) Likelihood ratio test p = 1.16e-01 Wald test p = 9.37e-02 Score (logrank) test p = 7.77e-02 HMGCR in OV (n=303): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.013 0.987 0.148 0.738 1.320 -0.089 0.929 Age 0.036 1.037 0.008 1.020 1.054 4.404 0.000 *** RaceBlack -0.050 0.951 0.577 0.307 2.949 -0.087 0.931 RaceWhite -0.158 0.854 0.515 0.311 2.345 -0.306 0.760 Purity -0.537 0.584 0.678 0.155 2.204 -0.793 0.428 Rsquare = 0.081 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.15e-03 Wald test p = 9.97e-04 Score (logrank) test p = 8.34e-04 HMGCR in PAAD (n=179): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.103 0.903 0.169 0.649 1.256 -0.608 0.543 Age 0.022 1.022 0.011 1.000 1.044 1.988 0.047 * Gendermale -0.209 0.812 0.216 0.531 1.240 -0.965 0.335 RaceBlack 0.005 1.005 0.739 0.236 4.279 0.006 0.995 RaceWhite 0.376 1.456 0.475 0.574 3.692 0.792 0.428 Stage2 0.589 1.803 0.440 0.761 4.272 1.338 0.181 Stage3 -0.341 0.711 1.103 0.082 6.184 -0.309 0.757 Stage4 0.291 1.338 0.829 0.264 6.792 0.352 0.725 Purity -0.693 0.500 0.409 0.224 1.114 -1.696 0.090 · Rsquare = 0.09 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.45e-02 Wald test p = 1.06e-01 Score (logrank) test p = 9.88e-02 HMGCR in PCPG (n=181): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 1.191 3.291 0.660 0.903 11.991 1.806 0.071 · Age 0.040 1.041 0.029 0.984 1.101 1.392 0.164 Gendermale 1.440 4.222 0.896 0.730 24.422 1.608 0.108 RaceBlack -1.685 0.185 31556.879 0.000 Inf 0.000 1.000 RaceWhite 17.948 62335615.057 28411.251 0.000 Inf 0.001 0.999 Purity 6.062 429.324 3.457 0.490 375865.330 1.754 0.079 · Rsquare = 0.07 (max possible = 3.07e-01 ) Likelihood ratio test p = 6.38e-02 Wald test p = 2.86e-01 Score (logrank) test p = 1.86e-01 HMGCR in PRAD (n=498): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.873 2.394 0.579 0.769 7.452 1.506 0.132 Age 0.016 1.016 0.054 0.914 1.129 0.295 0.768 RaceBlack 14.987 3225391.469 7111.276 0.000 Inf 0.002 0.998 RaceWhite 16.461 14095305.834 7111.276 0.000 Inf 0.002 0.998 Purity 0.661 1.937 1.420 0.120 31.313 0.465 0.642 Rsquare = 0.012 (max possible = 1.83e-01 ) Likelihood ratio test p = 4.08e-01 Wald test p = 5.03e-01 Score (logrank) test p = 4.46e-01 HMGCR in READ (n=166): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.259 1.295 0.500 0.486 3.450 0.518 0.605 Age 0.119 1.126 0.049 1.024 1.239 2.443 0.015 * Gendermale -0.444 0.642 0.706 0.161 2.562 -0.628 0.530 RaceBlack 13.027 454301.800 10244.096 0.000 Inf 0.001 0.999 RaceWhite 11.965 157188.191 10244.096 0.000 Inf 0.001 0.999 Stage2 -1.839 0.159 1.257 0.014 1.866 -1.464 0.143 Stage3 -0.401 0.670 0.908 0.113 3.972 -0.441 0.659 Stage4 -0.036 0.965 0.978 0.142 6.559 -0.037 0.971 Purity 0.338 1.402 1.388 0.092 21.279 0.243 0.808 Rsquare = 0.212 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.38e-02 Wald test p = 2.76e-01 Score (logrank) test p = 4.86e-02 HMGCR in SARC (n=260): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.402 1.495 0.145 1.125 1.986 2.773 0.006 ** Age 0.022 1.022 0.008 1.006 1.038 2.685 0.007 ** Gendermale 0.034 1.034 0.224 0.666 1.605 0.150 0.881 RaceBlack -0.082 0.921 1.086 0.110 7.745 -0.075 0.940 RaceWhite -0.559 0.572 1.023 0.077 4.245 -0.546 0.585 Purity 0.907 2.477 0.585 0.788 7.790 1.551 0.121 Rsquare = 0.073 (max possible = 9.75e-01 ) Likelihood ratio test p = 7.05e-03 Wald test p = 7.8e-03 Score (logrank) test p = 7.31e-03 HMGCR in SKCM (n=471): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.058 1.060 0.099 0.873 1.286 0.587 0.557 Age 0.018 1.018 0.005 1.008 1.029 3.527 0.000 *** Gendermale -0.063 0.939 0.159 0.687 1.282 -0.398 0.691 RaceWhite -1.342 0.261 0.412 0.116 0.587 -3.253 0.001 ** Stage2 0.281 1.324 0.219 0.863 2.032 1.284 0.199 Stage3 0.620 1.858 0.205 1.244 2.775 3.029 0.002 ** Stage4 1.358 3.888 0.352 1.950 7.755 3.855 0.000 *** Purity 0.984 2.676 0.344 1.362 5.257 2.858 0.004 ** Rsquare = 0.124 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.92e-08 Wald test p = 1.06e-08 Score (logrank) test p = 1.24e-09 HMGCR in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.319 1.375000e+00 0.281 0.793 2.385 1.135 0.257 Age 0.014 1.014000e+00 0.016 0.983 1.047 0.876 0.381 Gendermale 0.270 1.310000e+00 0.442 0.550 3.118 0.610 0.542 RaceWhite -1.258 2.840000e-01 0.634 0.082 0.985 -1.984 0.047 * Stage2 17.485 3.923103e+07 6242.661 0.000 Inf 0.003 0.998 Stage3 18.127 7.451624e+07 6242.661 0.000 Inf 0.003 0.998 Stage4 20.268 6.342128e+08 6242.661 0.000 Inf 0.003 0.997 Purity -0.086 9.180000e-01 0.981 0.134 6.271 -0.088 0.930 Rsquare = 0.159 (max possible = 8.69e-01 ) Likelihood ratio test p = 3.89e-02 Wald test p = 3.71e-02 Score (logrank) test p = 2.72e-03 HMGCR in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.036 1.036 0.110 0.835 1.286 0.324 0.746 Age 0.020 1.021 0.006 1.010 1.032 3.640 0.000 *** Gendermale -0.068 0.934 0.175 0.663 1.316 -0.391 0.696 RaceWhite -1.113 0.329 0.625 0.097 1.118 -1.781 0.075 · Stage2 0.156 1.168 0.231 0.743 1.836 0.675 0.500 Stage3 0.567 1.763 0.209 1.170 2.658 2.709 0.007 ** Stage4 1.139 3.122 0.400 1.425 6.841 2.845 0.004 ** Purity 1.121 3.069 0.376 1.469 6.412 2.983 0.003 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 1.05e-06 Wald test p = 1.66e-06 Score (logrank) test p = 6.43e-07 HMGCR in STAD (n=415): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.071 0.931 0.122 0.733 1.184 -0.581 0.561 Age 0.027 1.027 0.010 1.007 1.048 2.622 0.009 ** Gendermale 0.140 1.150 0.210 0.763 1.735 0.668 0.504 RaceBlack 0.265 1.304 0.448 0.542 3.135 0.593 0.553 RaceWhite 0.108 1.114 0.245 0.689 1.802 0.441 0.659 Stage2 0.497 1.644 0.390 0.765 3.534 1.273 0.203 Stage3 0.934 2.545 0.365 1.245 5.201 2.562 0.010 * Stage4 1.317 3.732 0.504 1.391 10.018 2.614 0.009 ** Purity -0.526 0.591 0.383 0.279 1.251 -1.375 0.169 Rsquare = 0.07 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.22e-02 Wald test p = 1.63e-02 Score (logrank) test p = 1.31e-02 HMGCR in TGCT (n=150): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 17.844 5.615874e+07 42036.961 0 Inf 0.000 1 Age -1.102 3.320000e-01 2003.707 0 Inf -0.001 1 RaceBlack 11.076 6.458386e+04 31554240.115 0 Inf 0.000 1 RaceWhite -30.667 0.000000e+00 35465929.470 0 Inf 0.000 1 Stage2 -9.071 0.000000e+00 48672.532 0 Inf 0.000 1 Stage3 -5.989 3.000000e-03 256367.342 0 Inf 0.000 1 Purity 30.263 1.390350e+13 265633.434 0 Inf 0.000 1 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 1.64e-03 HMGCR in THCA (n=509): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.658 1.931 0.370 0.935 3.989 1.778 0.075 · Age 0.132 1.142 0.028 1.081 1.205 4.781 0.000 *** Gendermale -0.335 0.715 0.672 0.191 2.671 -0.499 0.618 RaceBlack 17.443 37630677.561 8681.307 0.000 Inf 0.002 0.998 RaceWhite 17.198 29430428.631 8681.307 0.000 Inf 0.002 0.998 Stage2 0.342 1.408 1.098 0.164 12.116 0.312 0.755 Stage3 0.323 1.382 0.874 0.249 7.658 0.370 0.711 Stage4 2.099 8.157 1.019 1.106 60.135 2.059 0.039 * Purity 1.960 7.102 1.105 0.814 61.966 1.774 0.076 · Rsquare = 0.156 (max possible = 3.47e-01 ) Likelihood ratio test p = 5.9e-11 Wald test p = 1.58e-05 Score (logrank) test p = 8.12e-13 HMGCR in THYM (n=120): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -1.321 0.267 0.677 0.071 1.006 -1.952 0.051 · Age 0.053 1.054 0.035 0.985 1.128 1.523 0.128 Gendermale -0.220 0.802 0.754 0.183 3.513 -0.293 0.770 RaceBlack -16.263 0.000 10640.790 0.000 Inf -0.002 0.999 RaceWhite 0.466 1.594 1.091 0.188 13.522 0.427 0.669 Purity 0.248 1.281 1.141 0.137 11.991 0.217 0.828 Rsquare = 0.074 (max possible = 4.51e-01 ) Likelihood ratio test p = 1.95e-01 Wald test p = 2.98e-01 Score (logrank) test p = 1.77e-01 HMGCR in UCEC (n=545): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.118 0.889 0.134 0.683 1.157 -0.878 0.380 Age 0.052 1.053 0.016 1.021 1.087 3.224 0.001 ** RaceBlack -0.409 0.665 0.792 0.141 3.139 -0.516 0.606 RaceWhite -0.551 0.577 0.745 0.134 2.482 -0.739 0.460 Purity 0.348 1.417 0.656 0.391 5.127 0.531 0.596 Rsquare = 0.041 (max possible = 7.81e-01 ) Likelihood ratio test p = 3.71e-02 Wald test p = 4.78e-02 Score (logrank) test p = 4.46e-02 HMGCR in UCS (n=57): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR -0.279 0.756 0.270 0.446 1.283 -1.036 0.300 Age 0.047 1.048 0.024 1.000 1.098 1.961 0.050 · RaceBlack 17.528 40974441.374 6592.374 0.000 Inf 0.003 0.998 RaceWhite 17.861 57141449.767 6592.374 0.000 Inf 0.003 0.998 Purity -1.427 0.240 1.150 0.025 2.287 -1.241 0.215 Rsquare = 0.139 (max possible = 9.83e-01 ) Likelihood ratio test p = 1.7e-01 Wald test p = 2.33e-01 Score (logrank) test p = 1.64e-01 HMGCR in UVM (n=80): Model: Surv(OS, EVENT) ~ `HMGCR` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCR 0.433 1.542 0.316 0.830 2.867 1.369 0.171 Age 0.040 1.041 0.018 1.004 1.079 2.156 0.031 * Gendermale 0.259 1.296 0.473 0.513 3.272 0.548 0.584 Stage3 0.227 1.255 0.496 0.474 3.319 0.457 0.648 Stage4 3.893 49.050 1.220 4.487 536.197 3.190 0.001 ** Purity 1.960 7.100 1.273 0.586 85.984 1.540 0.123 Rsquare = 0.272 (max possible = 8.72e-01 ) Likelihood ratio test p = 4.4e-04 Wald test p = 2.33e-03 Score (logrank) test p = 1.52e-09 HMGCS1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.139 1.149 0.116 0.915 1.442 1.196 0.232 Age 0.007 1.007 0.014 0.979 1.034 0.467 0.640 Gendermale 0.461 1.585 0.425 0.689 3.648 1.084 0.279 RaceBlack -0.298 0.742 12145.393 0.000 Inf 0.000 1.000 RaceWhite 16.508 14773521.773 10356.910 0.000 Inf 0.002 0.999 Purity 2.555 12.873 2.375 0.123 1352.855 1.076 0.282 Rsquare = 0.088 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.36e-01 Wald test p = 7.45e-01 Score (logrank) test p = 5.79e-01 HMGCS1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.111 1.117 0.096 0.925 1.349 1.148 0.251 Age 0.033 1.034 0.009 1.016 1.051 3.846 0.000 *** Gendermale -0.193 0.825 0.180 0.580 1.173 -1.072 0.284 RaceBlack 0.631 1.879 0.451 0.776 4.551 1.398 0.162 RaceWhite 0.098 1.103 0.355 0.550 2.210 0.276 0.783 Stage2 14.597 2184582.246 1846.539 0.000 Inf 0.008 0.994 Stage3 15.072 3512820.562 1846.539 0.000 Inf 0.008 0.993 Stage4 15.608 6006078.204 1846.539 0.000 Inf 0.008 0.993 Purity 0.052 1.054 0.349 0.531 2.091 0.150 0.881 Rsquare = 0.133 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.15e-07 Wald test p = 9.93e-07 Score (logrank) test p = 2.55e-07 HMGCS1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.257 1.293 0.117 1.028 1.627 2.199 0.028 * Age 0.036 1.037 0.008 1.022 1.053 4.804 0.000 *** Gendermale 0.002 1.002 1.007 0.139 7.212 0.002 0.998 RaceBlack -0.044 0.957 0.619 0.284 3.220 -0.071 0.943 RaceWhite -0.283 0.754 0.596 0.234 2.426 -0.474 0.636 Stage2 0.419 1.521 0.304 0.838 2.759 1.380 0.168 Stage3 1.184 3.266 0.313 1.768 6.035 3.779 0.000 *** Stage4 2.632 13.904 0.393 6.442 30.008 6.706 0.000 *** Purity 0.467 1.596 0.424 0.695 3.664 1.102 0.270 Rsquare = 0.086 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.88e-13 Wald test p = 8.82e-17 Score (logrank) test p = 1.11e-22 HMGCS1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.307 7.350000e-01 0.257 0.444 1.217 -1.195 0.232 Age 0.014 1.014000e+00 0.018 0.979 1.050 0.779 0.436 RaceBlack -1.050 3.500000e-01 1.112 0.040 3.093 -0.944 0.345 RaceWhite -1.375 2.530000e-01 1.119 0.028 2.267 -1.229 0.219 Stage2 18.580 1.173028e+08 6514.499 0.000 Inf 0.003 0.998 Stage3 20.032 5.009130e+08 6514.499 0.000 Inf 0.003 0.998 Stage4 21.399 1.965094e+09 6514.499 0.000 Inf 0.003 0.997 Purity 0.931 2.538000e+00 0.947 0.397 16.238 0.983 0.325 Rsquare = 0.164 (max possible = 7.18e-01 ) Likelihood ratio test p = 2.92e-04 Wald test p = 5.09e-03 Score (logrank) test p = 2.55e-06 HMGCS1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.467 1.596000e+00 0.566 0.526 4.841 0.825 0.409 Age 0.020 1.020000e+00 0.031 0.959 1.085 0.637 0.524 RaceBlack -2.727 6.500000e-02 1.842 0.002 2.418 -1.481 0.139 RaceWhite -1.343 2.610000e-01 1.526 0.013 5.198 -0.880 0.379 Stage2 18.041 6.843075e+07 15215.082 0.000 Inf 0.001 0.999 Stage3 19.635 3.369502e+08 15215.082 0.000 Inf 0.001 0.999 Stage4 52.121 4.324605e+22 2045650.966 0.000 Inf 0.000 1.000 Purity 4.118 6.143300e+01 2.771 0.269 14024.950 1.486 0.137 Rsquare = 0.378 (max possible = 6.68e-01 ) Likelihood ratio test p = 4.72e-04 Wald test p = 1e+00 Score (logrank) test p = 3.59e-14 HMGCS1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.178 1.195 0.216 0.782 1.825 0.823 0.410 Age 0.049 1.051 0.012 1.026 1.076 4.111 0.000 *** Gendermale -15.496 0.000 3464.280 0.000 Inf -0.004 0.996 RaceBlack -0.462 0.630 1.173 0.063 6.281 -0.394 0.694 RaceWhite 0.142 1.152 1.039 0.150 8.834 0.136 0.891 Stage2 0.351 1.420 0.375 0.680 2.964 0.934 0.350 Stage3 0.919 2.507 0.401 1.142 5.504 2.290 0.022 * Stage4 2.188 8.922 0.594 2.783 28.599 3.682 0.000 *** Purity 0.311 1.365 0.616 0.408 4.566 0.505 0.614 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 7.93e-05 Wald test p = 1.7e-05 Score (logrank) test p = 3.12e-07 HMGCS1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.342 1.408 0.314 0.762 2.604 1.092 0.275 Age 0.049 1.050 0.021 1.007 1.095 2.310 0.021 * Gendermale 1.104 3.018 1.117 0.338 26.943 0.989 0.323 RaceBlack 16.637 16793702.328 7002.010 0.000 Inf 0.002 0.998 RaceWhite 16.070 9531015.169 7002.010 0.000 Inf 0.002 0.998 Stage2 0.575 1.777 1.078 0.215 14.699 0.533 0.594 Stage3 1.413 4.108 1.073 0.501 33.661 1.317 0.188 Stage4 2.317 10.148 1.200 0.966 106.610 1.931 0.053 · Purity 1.154 3.170 1.346 0.227 44.346 0.857 0.391 Rsquare = 0.112 (max possible = 6.98e-01 ) Likelihood ratio test p = 2.81e-02 Wald test p = 4.72e-02 Score (logrank) test p = 1.39e-02 HMGCS1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.197 1.218 0.134 0.936 1.584 1.468 0.142 Age 0.009 1.009 0.010 0.990 1.029 0.938 0.348 RaceBlack 1.037 2.820 1.067 0.348 22.828 0.972 0.331 RaceWhite 0.787 2.196 1.015 0.300 16.058 0.775 0.438 Purity 0.532 1.703 0.741 0.398 7.281 0.718 0.473 Rsquare = 0.023 (max possible = 8.91e-01 ) Likelihood ratio test p = 3.87e-01 Wald test p = 4.1e-01 Score (logrank) test p = 4.01e-01 HMGCS1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.747 2.110 0.497 0.797 5.586 1.504 0.133 Age 0.013 1.013 0.021 0.972 1.056 0.625 0.532 Gendermale 0.009 1.009 0.582 0.323 3.154 0.015 0.988 RaceBlack -2.876 0.056 2.284 0.001 4.958 -1.259 0.208 RaceWhite -1.247 0.287 0.885 0.051 1.629 -1.409 0.159 Stage2 0.135 1.145 0.732 0.273 4.807 0.185 0.853 Stage3 -13.679 0.000 7188.948 0.000 Inf -0.002 0.998 Stage4 0.652 1.919 0.695 0.492 7.488 0.939 0.348 Purity 3.443 31.291 1.816 0.891 1098.839 1.896 0.058 · Rsquare = 0.26 (max possible = 9.46e-01 ) Likelihood ratio test p = 2.85e-01 Wald test p = 4.6e-01 Score (logrank) test p = 2.77e-01 HMGCS1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.015 0.985 0.157 0.724 1.341 -0.095 0.924 Age 0.024 1.024 0.011 1.001 1.047 2.076 0.038 * Gendermale 0.215 1.239 0.269 0.731 2.100 0.797 0.425 RaceBlack -0.424 0.654 0.833 0.128 3.349 -0.509 0.611 RaceWhite -0.454 0.635 0.783 0.137 2.948 -0.579 0.562 Stage2 0.210 1.233 0.562 0.410 3.712 0.373 0.709 Stage3 0.807 2.241 0.549 0.764 6.578 1.469 0.142 Stage4 1.886 6.590 0.553 2.231 19.462 3.413 0.001 ** Purity -0.233 0.792 0.608 0.241 2.610 -0.383 0.702 Rsquare = 0.109 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.63e-04 Wald test p = 1.58e-04 Score (logrank) test p = 2.33e-05 HMGCS1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 1.821 6.178 1.038 0.808 47.225 1.755 0.079 · Age -0.014 0.986 0.048 0.897 1.084 -0.293 0.769 Gendermale 1.025 2.788 1.124 0.308 25.245 0.912 0.362 RaceBlack 2.066 7.891 2.103 0.128 486.653 0.982 0.326 RaceWhite -3.118 0.044 1.497 0.002 0.832 -2.083 0.037 * Purity -2.332 0.097 2.127 0.002 6.277 -1.096 0.273 Rsquare = 0.207 (max possible = 5.58e-01 ) Likelihood ratio test p = 1.47e-01 Wald test p = 3.82e-01 Score (logrank) test p = 1.65e-01 HMGCS1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.077 0.926 0.152 0.688 1.246 -0.508 0.611 Age 0.011 1.011 0.014 0.983 1.039 0.751 0.453 Gendermale 0.458 1.581 0.538 0.551 4.541 0.851 0.395 RaceBlack 0.382 1.465 1.072 0.179 11.967 0.356 0.722 RaceWhite -0.104 0.902 0.448 0.375 2.169 -0.231 0.817 Stage2 0.708 2.030 0.653 0.564 7.301 1.084 0.278 Stage3 1.478 4.383 0.671 1.177 16.318 2.204 0.028 * Stage4 2.922 18.585 0.784 3.996 86.438 3.726 0.000 *** Purity 0.294 1.342 0.783 0.289 6.219 0.376 0.707 Rsquare = 0.143 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.05e-02 Wald test p = 5.14e-03 Score (logrank) test p = 4.22e-04 HMGCS1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.013 1.013 0.124 0.794 1.293 0.107 0.914 Age 0.030 1.030 0.008 1.013 1.047 3.579 0.000 *** Gendermale -0.095 0.910 0.213 0.599 1.381 -0.444 0.657 RaceBlack 0.528 1.695 0.726 0.408 7.041 0.727 0.467 RaceWhite -0.242 0.785 0.614 0.235 2.617 -0.394 0.694 Purity -1.115 0.328 0.594 0.102 1.051 -1.876 0.061 · Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.78e-03 Wald test p = 6.72e-03 Score (logrank) test p = 5.81e-03 HMGCS1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.132 1.141 0.084 0.968 1.345 1.569 0.117 Age 0.021 1.021 0.008 1.006 1.037 2.802 0.005 ** Gendermale -0.227 0.797 0.172 0.569 1.117 -1.317 0.188 RaceBlack 0.027 1.027 0.562 0.341 3.091 0.048 0.962 RaceWhite -0.288 0.750 0.511 0.275 2.041 -0.564 0.573 Stage2 0.634 1.886 0.544 0.649 5.474 1.166 0.243 Stage3 0.860 2.363 0.536 0.826 6.759 1.604 0.109 Stage4 1.264 3.538 0.510 1.302 9.613 2.478 0.013 * Purity -0.107 0.899 0.367 0.438 1.845 -0.292 0.771 Rsquare = 0.075 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.01e-04 Wald test p = 5.65e-04 Score (logrank) test p = 4.09e-04 HMGCS1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.290 1.337000e+00 0.289 0.759 2.354 1.006 0.315 Age 0.001 1.001000e+00 0.027 0.949 1.056 0.039 0.969 Gendermale -0.107 8.990000e-01 0.546 0.308 2.622 -0.196 0.845 RaceBlack 18.838 1.518546e+08 12154.488 0.000 Inf 0.002 0.999 RaceWhite 17.846 5.630160e+07 12154.488 0.000 Inf 0.001 0.999 Stage2 17.497 3.971149e+07 5428.452 0.000 Inf 0.003 0.997 Stage3 16.685 1.762701e+07 5428.452 0.000 Inf 0.003 0.998 Stage4 17.515 4.042408e+07 5428.452 0.000 Inf 0.003 0.997 Purity -1.649 1.920000e-01 1.088 0.023 1.620 -1.516 0.129 Rsquare = 0.101 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.44e-01 Wald test p = 9.06e-01 Score (logrank) test p = 7.91e-01 HMGCS1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.127 1.136 0.091 0.951 1.357 1.405 0.160 Age 0.027 1.027 0.008 1.010 1.044 3.181 0.001 ** Gendermale -0.267 0.765 0.183 0.534 1.096 -1.460 0.144 RaceBlack -0.136 0.872 0.570 0.286 2.664 -0.240 0.811 RaceWhite -0.434 0.648 0.513 0.237 1.769 -0.847 0.397 Stage2 0.366 1.442 0.554 0.487 4.270 0.661 0.509 Stage3 0.712 2.038 0.541 0.706 5.880 1.316 0.188 Stage4 1.147 3.149 0.512 1.155 8.589 2.241 0.025 * Purity 0.163 1.177 0.404 0.533 2.597 0.404 0.686 Rsquare = 0.09 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.56e-04 Wald test p = 4.81e-04 Score (logrank) test p = 3.61e-04 HMGCS1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p HMGCS1 1.113 3.045 0.650 0.851 1.088900e+01 1.712 0.087 Age 0.077 1.081 0.028 1.022 1.142000e+00 2.739 0.006 Gendermale -0.997 0.369 0.727 0.089 1.534000e+00 -1.371 0.170 RaceBlack -14.383 0.000 3119.914 0.000 Inf -0.005 0.996 RaceWhite -0.874 0.417 1.158 0.043 4.035000e+00 -0.755 0.450 Stage2 14.215 1490750.766 0.845 284296.195 7.816981e+06 16.814 0.000 Stage3 15.260 4240818.937 0.780 919126.459 1.956700e+07 19.560 0.000 Stage4 17.637 45687408.523 0.897 7869002.996 2.652610e+08 19.654 0.000 Purity 0.628 1.874 3.154 0.004 9.061050e+02 0.199 0.842 signif HMGCS1 · Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.371 (max possible = 6.71e-01 ) Likelihood ratio test p = 6.02e-04 Wald test p = 2.12e-223 Score (logrank) test p = 5.45e-09 HMGCS1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.471 0.624 0.143 0.472 0.826 -3.298 0.001 ** Age 0.036 1.037 0.008 1.020 1.053 4.325 0.000 *** Gendermale -0.112 0.894 0.186 0.621 1.286 -0.604 0.546 RaceBlack 0.191 1.211 1.056 0.153 9.593 0.181 0.856 RaceWhite 0.161 1.175 1.015 0.161 8.594 0.159 0.874 Stage2 0.219 1.245 0.345 0.633 2.448 0.634 0.526 Stage3 0.677 1.968 0.234 1.244 3.114 2.895 0.004 ** Stage4 1.690 5.422 0.217 3.546 8.290 7.803 0.000 *** Purity -0.059 0.943 0.359 0.467 1.906 -0.163 0.870 Rsquare = 0.193 (max possible = 9.65e-01 ) Likelihood ratio test p = 7.35e-17 Wald test p = 5.17e-17 Score (logrank) test p = 2.18e-20 HMGCS1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.267 1.307 0.283 0.751 2.274 0.946 0.344 Age 0.008 1.008 0.016 0.978 1.040 0.529 0.597 Gendermale -0.576 0.562 0.390 0.262 1.208 -1.476 0.140 RaceBlack -1.970 0.140 1.196 0.013 1.455 -1.647 0.100 RaceWhite -2.013 0.134 1.183 0.013 1.359 -1.701 0.089 · Stage2 -0.435 0.647 1.055 0.082 5.125 -0.412 0.680 Stage3 1.724 5.610 0.439 2.372 13.265 3.927 0.000 *** Stage4 2.837 17.060 0.532 6.018 48.365 5.336 0.000 *** Purity -0.408 0.665 0.759 0.150 2.943 -0.538 0.591 Rsquare = 0.167 (max possible = 7.58e-01 ) Likelihood ratio test p = 7.96e-06 Wald test p = 3.96e-06 Score (logrank) test p = 5.89e-10 HMGCS1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.263 1.301 0.172 0.928 1.823 1.528 0.126 Age 0.037 1.038 0.008 1.021 1.055 4.532 0.000 *** Gendermale -0.160 0.852 0.212 0.562 1.292 -0.754 0.451 RaceBlack -0.050 0.951 1.120 0.106 8.547 -0.045 0.964 RaceWhite -0.595 0.552 1.020 0.075 4.074 -0.583 0.560 Rsquare = 0.168 (max possible = 9.96e-01 ) Likelihood ratio test p = 4.63e-05 Wald test p = 1.64e-04 Score (logrank) test p = 1.13e-04 HMGCS1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.226 0.798 0.111 0.642 0.992 -2.036 0.042 * Age 0.061 1.063 0.008 1.047 1.079 7.946 0.000 *** Gendermale 0.034 1.035 0.196 0.704 1.521 0.173 0.863 RaceBlack 15.313 4468709.033 2072.505 0.000 Inf 0.007 0.994 RaceWhite 15.344 4609030.892 2072.505 0.000 Inf 0.007 0.994 Purity -0.801 0.449 0.413 0.200 1.009 -1.938 0.053 · Rsquare = 0.144 (max possible = 9.07e-01 ) Likelihood ratio test p = 1.58e-13 Wald test p = 2.2e-13 Score (logrank) test p = 1.74e-14 HMGCS1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.071 0.932 0.096 0.772 1.125 -0.733 0.463 Age 0.012 1.012 0.008 0.996 1.028 1.423 0.155 Gendermale -0.126 0.881 0.225 0.567 1.370 -0.561 0.575 RaceBlack 0.920 2.509 0.490 0.960 6.557 1.876 0.061 · RaceWhite 0.006 1.006 0.237 0.633 1.600 0.026 0.979 Stage2 0.290 1.336 0.263 0.797 2.238 1.100 0.271 Stage3 0.968 2.633 0.236 1.657 4.185 4.096 0.000 *** Stage4 1.585 4.877 0.619 1.450 16.407 2.560 0.010 * Purity 0.646 1.908 0.467 0.763 4.770 1.383 0.167 Rsquare = 0.086 (max possible = 9.66e-01 ) Likelihood ratio test p = 9.81e-04 Wald test p = 6.46e-04 Score (logrank) test p = 2.34e-04 HMGCS1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.195 1.215 0.088 1.022 1.445 2.201 0.028 * Age 0.009 1.009 0.009 0.991 1.027 0.987 0.323 Gendermale -0.008 0.992 0.169 0.713 1.382 -0.046 0.963 RaceBlack 16.129 10105877.481 1891.727 0.000 Inf 0.009 0.993 RaceWhite 16.311 12133667.835 1891.727 0.000 Inf 0.009 0.993 Stage2 0.859 2.362 0.201 1.592 3.503 4.272 0.000 *** Stage3 0.944 2.570 0.220 1.669 3.959 4.283 0.000 *** Stage4 1.055 2.873 0.335 1.489 5.543 3.146 0.002 ** Purity 0.620 1.858 0.345 0.945 3.654 1.797 0.072 · Rsquare = 0.107 (max possible = 9.74e-01 ) Likelihood ratio test p = 3.23e-07 Wald test p = 4.59e-06 Score (logrank) test p = 4.85e-07 HMGCS1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.001 1.001 0.079 0.857 1.169 0.011 0.991 Age 0.016 1.016 0.009 0.998 1.035 1.724 0.085 · Gendermale 0.436 1.546 0.197 1.051 2.274 2.213 0.027 * RaceBlack 0.007 1.007 0.608 0.306 3.318 0.012 0.991 RaceWhite -0.519 0.595 0.564 0.197 1.798 -0.920 0.357 Stage2 0.211 1.235 0.188 0.855 1.786 1.125 0.261 Stage3 0.603 1.828 0.215 1.201 2.785 2.812 0.005 ** Stage4 0.759 2.137 0.795 0.450 10.149 0.955 0.340 Purity -0.346 0.708 0.368 0.344 1.456 -0.939 0.347 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.42e-02 Wald test p = 1.87e-02 Score (logrank) test p = 1.61e-02 HMGCS1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.710 2.033 0.211 1.346 3.073 3.369 0.001 ** Age 0.006 1.006 0.016 0.976 1.038 0.394 0.694 Gendermale -0.275 0.760 0.340 0.390 1.481 -0.806 0.420 RaceBlack -0.843 0.430 1.549 0.021 8.969 -0.544 0.586 RaceWhite -0.691 0.501 1.048 0.064 3.907 -0.659 0.510 Stage2 -0.005 0.995 0.458 0.405 2.444 -0.010 0.992 Stage3 -0.234 0.791 0.408 0.356 1.760 -0.575 0.565 Stage4 -0.257 0.774 0.464 0.311 1.922 -0.553 0.580 Purity -0.930 0.395 0.588 0.125 1.250 -1.580 0.114 Rsquare = 0.185 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.33e-02 Wald test p = 7.02e-02 Score (logrank) test p = 5.47e-02 HMGCS1 in OV (n=303): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.091 1.095 0.101 0.898 1.335 0.899 0.369 Age 0.036 1.037 0.008 1.020 1.053 4.423 0.000 *** RaceBlack -0.034 0.966 0.577 0.312 2.992 -0.060 0.952 RaceWhite -0.141 0.869 0.516 0.316 2.387 -0.273 0.785 Purity -0.623 0.536 0.676 0.143 2.017 -0.922 0.357 Rsquare = 0.084 (max possible = 9.97e-01 ) Likelihood ratio test p = 8.2e-04 Wald test p = 7.24e-04 Score (logrank) test p = 5.86e-04 HMGCS1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.042 1.043 0.135 0.800 1.361 0.313 0.754 Age 0.022 1.022 0.011 1.000 1.044 1.987 0.047 * Gendermale -0.211 0.810 0.217 0.529 1.239 -0.972 0.331 RaceBlack 0.000 1.000 0.741 0.234 4.270 -0.001 1.000 RaceWhite 0.381 1.464 0.479 0.573 3.742 0.796 0.426 Stage2 0.608 1.837 0.440 0.776 4.351 1.382 0.167 Stage3 -0.236 0.790 1.091 0.093 6.708 -0.216 0.829 Stage4 0.186 1.204 0.840 0.232 6.245 0.221 0.825 Purity -0.649 0.522 0.414 0.232 1.177 -1.567 0.117 Rsquare = 0.089 (max possible = 9.91e-01 ) Likelihood ratio test p = 8.09e-02 Wald test p = 1.16e-01 Score (logrank) test p = 1.12e-01 HMGCS1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.406 1.500 0.590 0.472 4.773 0.687 0.492 Age 0.038 1.039 0.028 0.983 1.098 1.361 0.174 Gendermale 1.385 3.996 0.882 0.709 22.506 1.571 0.116 RaceBlack -0.737 0.478 19371.912 0.000 Inf 0.000 1.000 RaceWhite 17.078 26121846.405 16046.348 0.000 Inf 0.001 0.999 Purity 6.079 436.381 3.605 0.373 510623.458 1.686 0.092 · Rsquare = 0.057 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.4e-01 Wald test p = 3.82e-01 Score (logrank) test p = 2.93e-01 HMGCS1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.375 1.455 0.340 0.747 2.837 1.102 0.270 Age 0.012 1.012 0.057 0.906 1.131 0.213 0.832 RaceBlack 15.295 4391798.177 6782.201 0.000 Inf 0.002 0.998 RaceWhite 16.317 12204695.947 6782.201 0.000 Inf 0.002 0.998 Purity 1.117 3.054 1.407 0.194 48.113 0.794 0.427 Rsquare = 0.01 (max possible = 1.83e-01 ) Likelihood ratio test p = 5.6e-01 Wald test p = 6.51e-01 Score (logrank) test p = 5.59e-01 HMGCS1 in READ (n=166): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.088 1.092 0.558 0.366 3.257 0.158 0.874 Age 0.111 1.118 0.046 1.022 1.223 2.440 0.015 * Gendermale -0.379 0.684 0.710 0.170 2.753 -0.534 0.593 RaceBlack 13.261 574247.560 10146.685 0.000 Inf 0.001 0.999 RaceWhite 12.246 208095.219 10146.685 0.000 Inf 0.001 0.999 Stage2 -1.841 0.159 1.254 0.014 1.854 -1.468 0.142 Stage3 -0.456 0.634 0.915 0.105 3.807 -0.499 0.618 Stage4 -0.125 0.882 0.965 0.133 5.846 -0.130 0.897 Purity 0.144 1.155 1.329 0.085 15.622 0.108 0.914 Rsquare = 0.21 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.67e-02 Wald test p = 2.44e-01 Score (logrank) test p = 4.92e-02 HMGCS1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.359 1.432 0.132 1.106 1.854 2.723 0.006 ** Age 0.020 1.020 0.008 1.003 1.037 2.361 0.018 * Gendermale 0.093 1.098 0.228 0.703 1.714 0.409 0.682 RaceBlack -0.147 0.863 1.086 0.103 7.255 -0.135 0.892 RaceWhite -0.549 0.578 1.024 0.078 4.296 -0.536 0.592 Purity 0.826 2.284 0.570 0.747 6.987 1.448 0.148 Rsquare = 0.072 (max possible = 9.75e-01 ) Likelihood ratio test p = 8.2e-03 Wald test p = 8.95e-03 Score (logrank) test p = 8.37e-03 HMGCS1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.060 0.942 0.085 0.798 1.112 -0.707 0.480 Age 0.019 1.019 0.005 1.008 1.029 3.575 0.000 *** Gendermale -0.037 0.963 0.158 0.707 1.314 -0.236 0.814 RaceWhite -1.249 0.287 0.405 0.130 0.635 -3.081 0.002 ** Stage2 0.272 1.312 0.218 0.855 2.014 1.245 0.213 Stage3 0.594 1.811 0.206 1.210 2.710 2.888 0.004 ** Stage4 1.352 3.863 0.352 1.939 7.695 3.844 0.000 *** Purity 1.046 2.846 0.343 1.452 5.576 3.047 0.002 ** Rsquare = 0.125 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.78e-08 Wald test p = 8.97e-09 Score (logrank) test p = 1.11e-09 HMGCS1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.238 1.268000e+00 0.218 0.827 1.944 1.090 0.276 Age 0.015 1.015000e+00 0.016 0.984 1.047 0.917 0.359 Gendermale 0.236 1.266000e+00 0.438 0.536 2.991 0.538 0.590 RaceWhite -1.147 3.180000e-01 0.632 0.092 1.095 -1.816 0.069 · Stage2 17.456 3.810138e+07 6233.139 0.000 Inf 0.003 0.998 Stage3 18.099 7.249463e+07 6233.139 0.000 Inf 0.003 0.998 Stage4 20.036 5.031656e+08 6233.139 0.000 Inf 0.003 0.997 Purity -0.148 8.630000e-01 1.011 0.119 6.261 -0.146 0.884 Rsquare = 0.157 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.1e-02 Wald test p = 4.29e-02 Score (logrank) test p = 3.02e-03 HMGCS1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.119 0.888 0.097 0.735 1.074 -1.227 0.220 Age 0.021 1.021 0.006 1.010 1.033 3.723 0.000 *** Gendermale -0.026 0.975 0.174 0.693 1.370 -0.147 0.883 RaceWhite -0.892 0.410 0.614 0.123 1.366 -1.452 0.147 Stage2 0.146 1.157 0.231 0.736 1.818 0.633 0.527 Stage3 0.534 1.706 0.211 1.129 2.578 2.538 0.011 * Stage4 1.131 3.100 0.399 1.417 6.781 2.832 0.005 ** Purity 1.195 3.303 0.374 1.586 6.881 3.191 0.001 ** Rsquare = 0.138 (max possible = 9.95e-01 ) Likelihood ratio test p = 5.65e-07 Wald test p = 8.74e-07 Score (logrank) test p = 3.45e-07 HMGCS1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.086 0.918 0.101 0.753 1.119 -0.846 0.397 Age 0.028 1.028 0.010 1.008 1.049 2.687 0.007 ** Gendermale 0.151 1.163 0.210 0.771 1.755 0.719 0.472 RaceBlack 0.312 1.367 0.451 0.565 3.305 0.693 0.488 RaceWhite 0.114 1.121 0.245 0.693 1.812 0.465 0.642 Stage2 0.507 1.660 0.391 0.772 3.570 1.298 0.194 Stage3 0.937 2.554 0.364 1.250 5.217 2.572 0.010 * Stage4 1.343 3.831 0.505 1.423 10.308 2.659 0.008 ** Purity -0.499 0.607 0.385 0.286 1.291 -1.297 0.195 Rsquare = 0.072 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.07e-02 Wald test p = 1.47e-02 Score (logrank) test p = 1.19e-02 HMGCS1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 3.418 30.498 28754.784 0 Inf 0.000 1.000 Age -1.558 0.211 2000.401 0 Inf -0.001 0.999 RaceBlack -0.196 0.822 12494486.485 0 Inf 0.000 1.000 RaceWhite -41.002 0.000 33124640.942 0 Inf 0.000 1.000 Stage2 -4.722 0.009 47784.659 0 Inf 0.000 1.000 Stage3 11.340 84131.315 138640.604 0 Inf 0.000 1.000 Purity 8.572 5284.155 243403.357 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 1.66e-03 HMGCS1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.949 2.583 0.554 0.872 7.653 1.712 0.087 · Age 0.142 1.153 0.029 1.090 1.219 4.969 0.000 *** Gendermale -0.239 0.787 0.648 0.221 2.804 -0.369 0.712 RaceBlack 16.914 22162246.322 6194.054 0.000 Inf 0.003 0.998 RaceWhite 16.523 14992989.717 6194.054 0.000 Inf 0.003 0.998 Stage2 0.673 1.960 1.139 0.210 18.276 0.591 0.555 Stage3 0.407 1.503 0.870 0.273 8.271 0.468 0.640 Stage4 2.421 11.257 1.099 1.306 97.022 2.203 0.028 * Purity 2.285 9.821 1.123 1.088 88.646 2.035 0.042 * Rsquare = 0.156 (max possible = 3.47e-01 ) Likelihood ratio test p = 6.35e-11 Wald test p = 1.35e-04 Score (logrank) test p = 3.31e-11 HMGCS1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.660 0.517 0.597 0.160 1.665 -1.106 0.269 Age 0.056 1.058 0.033 0.991 1.130 1.688 0.091 · Gendermale -0.263 0.769 0.751 0.177 3.347 -0.350 0.726 RaceBlack -16.496 0.000 9810.662 0.000 Inf -0.002 0.999 RaceWhite 0.551 1.735 1.095 0.203 14.829 0.503 0.615 Purity 0.495 1.641 1.116 0.184 14.608 0.444 0.657 Rsquare = 0.054 (max possible = 4.51e-01 ) Likelihood ratio test p = 3.96e-01 Wald test p = 5.47e-01 Score (logrank) test p = 4.27e-01 HMGCS1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.329 1.390 0.173 0.989 1.953 1.897 0.058 · Age 0.048 1.049 0.016 1.017 1.082 3.040 0.002 ** RaceBlack -0.554 0.575 0.799 0.120 2.752 -0.693 0.488 RaceWhite -0.627 0.534 0.748 0.123 2.311 -0.839 0.401 Purity 0.400 1.492 0.649 0.418 5.328 0.616 0.538 Rsquare = 0.05 (max possible = 7.81e-01 ) Likelihood ratio test p = 1.26e-02 Wald test p = 1.43e-02 Score (logrank) test p = 1.35e-02 HMGCS1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 -0.077 0.925 0.257 0.560 1.530 -0.302 0.763 Age 0.045 1.046 0.024 0.997 1.097 1.831 0.067 · RaceBlack 17.543 41571869.407 6475.136 0.000 Inf 0.003 0.998 RaceWhite 17.810 54271945.395 6475.136 0.000 Inf 0.003 0.998 Purity -0.836 0.434 1.058 0.055 3.448 -0.790 0.430 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.46e-01 Wald test p = 3.58e-01 Score (logrank) test p = 2.63e-01 HMGCS1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `HMGCS1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif HMGCS1 0.179 1.196 0.276 0.697 2.053 0.649 0.516 Age 0.040 1.041 0.019 1.003 1.080 2.125 0.034 * Gendermale 0.253 1.288 0.479 0.503 3.295 0.528 0.598 Stage3 0.305 1.356 0.503 0.506 3.634 0.606 0.544 Stage4 3.841 46.579 1.220 4.266 508.610 3.149 0.002 ** Purity 1.996 7.357 1.258 0.625 86.667 1.586 0.113 Rsquare = 0.257 (max possible = 8.72e-01 ) Likelihood ratio test p = 8.4e-04 Wald test p = 3.72e-03 Score (logrank) test p = 2.6e-09 IDI1 in ACC (n=79): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.307 1.359 0.156 1.002 1.844 1.974 0.048 * Age 0.010 1.010 0.014 0.982 1.039 0.673 0.501 Gendermale 0.619 1.858 0.438 0.788 4.380 1.415 0.157 RaceBlack -0.669 0.512 12469.780 0.000 Inf 0.000 1.000 RaceWhite 16.386 13074508.720 10655.318 0.000 Inf 0.002 0.999 Purity 2.000 7.388 2.288 0.083 655.011 0.874 0.382 Rsquare = 0.127 (max possible = 9.38e-01 ) Likelihood ratio test p = 1.93e-01 Wald test p = 4.21e-01 Score (logrank) test p = 2.95e-01 IDI1 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.157 1.171 0.115 0.935 1.466 1.371 0.170 Age 0.033 1.033 0.009 1.016 1.051 3.822 0.000 *** Gendermale -0.186 0.830 0.179 0.585 1.179 -1.039 0.299 RaceBlack 0.628 1.873 0.450 0.775 4.530 1.394 0.163 RaceWhite 0.063 1.065 0.357 0.530 2.142 0.177 0.860 Stage2 14.473 1930275.606 1861.257 0.000 Inf 0.008 0.994 Stage3 14.936 3066195.006 1861.257 0.000 Inf 0.008 0.994 Stage4 15.423 4991382.045 1861.257 0.000 Inf 0.008 0.993 Purity 0.129 1.138 0.342 0.582 2.223 0.378 0.705 Rsquare = 0.135 (max possible = 9.91e-01 ) Likelihood ratio test p = 9.03e-08 Wald test p = 5.7e-07 Score (logrank) test p = 1.7e-07 IDI1 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.201 1.222 0.116 0.974 1.533 1.734 0.083 · Age 0.037 1.037 0.008 1.022 1.053 4.855 0.000 *** Gendermale 0.057 1.059 1.007 0.147 7.624 0.057 0.955 RaceBlack -0.033 0.968 0.619 0.287 3.259 -0.053 0.958 RaceWhite -0.284 0.753 0.597 0.234 2.425 -0.476 0.634 Stage2 0.409 1.505 0.304 0.830 2.730 1.346 0.178 Stage3 1.162 3.197 0.313 1.730 5.909 3.708 0.000 *** Stage4 2.492 12.083 0.389 5.636 25.903 6.404 0.000 *** Purity 0.402 1.495 0.425 0.650 3.441 0.946 0.344 Rsquare = 0.084 (max possible = 7.85e-01 ) Likelihood ratio test p = 6.52e-13 Wald test p = 1.71e-16 Score (logrank) test p = 1.77e-22 IDI1 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.019 9.810000e-01 0.267 0.582 1.654 -0.072 0.943 Age 0.011 1.011000e+00 0.018 0.977 1.046 0.613 0.540 RaceBlack -0.917 4.000000e-01 1.108 0.046 3.506 -0.828 0.408 RaceWhite -1.234 2.910000e-01 1.119 0.032 2.606 -1.104 0.270 Stage2 18.688 1.306625e+08 6491.543 0.000 Inf 0.003 0.998 Stage3 20.112 5.427826e+08 6491.543 0.000 Inf 0.003 0.998 Stage4 21.409 1.985730e+09 6491.543 0.000 Inf 0.003 0.997 Purity 0.760 2.139000e+00 0.959 0.327 14.005 0.793 0.428 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.21e-04 Wald test p = 7.18e-03 Score (logrank) test p = 4.72e-06 IDI1 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.166 1.181000e+00 0.423 0.515 2.705 0.393 0.694 Age 0.031 1.032000e+00 0.028 0.977 1.090 1.115 0.265 RaceBlack -2.885 5.600000e-02 1.841 0.002 2.060 -1.567 0.117 RaceWhite -1.594 2.030000e-01 1.478 0.011 3.678 -1.079 0.281 Stage2 18.149 7.623810e+07 15075.509 0.000 Inf 0.001 0.999 Stage3 19.744 3.755573e+08 15075.509 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 2.966 1.941200e+01 2.270 0.227 1660.545 1.307 0.191 Rsquare = 0.372 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.81e-04 Wald test p = 2.28e-01 Score (logrank) test p = 1.07e-14 IDI1 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.140 1.150 0.225 0.740 1.788 0.622 0.534 Age 0.049 1.050 0.012 1.026 1.075 4.117 0.000 *** Gendermale -15.358 0.000 3457.907 0.000 Inf -0.004 0.996 RaceBlack -0.432 0.649 1.174 0.065 6.474 -0.368 0.713 RaceWhite 0.176 1.192 1.037 0.156 9.107 0.169 0.865 Stage2 0.336 1.399 0.374 0.672 2.915 0.897 0.370 Stage3 0.869 2.384 0.393 1.102 5.154 2.208 0.027 * Stage4 2.189 8.925 0.595 2.781 28.644 3.679 0.000 *** Purity 0.261 1.298 0.618 0.386 4.363 0.422 0.673 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 8.93e-05 Wald test p = 1.7e-05 Score (logrank) test p = 3.24e-07 IDI1 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.094 1.098 0.316 0.592 2.039 0.297 0.766 Age 0.050 1.051 0.021 1.009 1.095 2.404 0.016 * Gendermale 1.005 2.733 1.111 0.310 24.122 0.905 0.365 RaceBlack 16.573 15761950.439 6574.505 0.000 Inf 0.003 0.998 RaceWhite 15.963 8565746.771 6574.505 0.000 Inf 0.002 0.998 Stage2 0.666 1.946 1.074 0.237 15.973 0.620 0.535 Stage3 1.556 4.740 1.071 0.581 38.660 1.453 0.146 Stage4 2.073 7.952 1.173 0.798 79.189 1.768 0.077 · Purity 1.060 2.887 1.319 0.218 38.295 0.804 0.422 Rsquare = 0.106 (max possible = 6.98e-01 ) Likelihood ratio test p = 4.09e-02 Wald test p = 6.95e-02 Score (logrank) test p = 2.33e-02 IDI1 in CESC (n=306): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.237 1.267 0.193 0.869 1.849 1.231 0.218 Age 0.011 1.011 0.010 0.991 1.031 1.092 0.275 RaceBlack 1.129 3.093 1.069 0.380 25.161 1.056 0.291 RaceWhite 0.849 2.336 1.015 0.319 17.086 0.836 0.403 Purity 0.537 1.710 0.734 0.405 7.215 0.731 0.465 Rsquare = 0.02 (max possible = 8.91e-01 ) Likelihood ratio test p = 4.59e-01 Wald test p = 4.87e-01 Score (logrank) test p = 4.76e-01 IDI1 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.065 0.937 0.443 0.394 2.231 -0.147 0.883 Age 0.017 1.018 0.022 0.975 1.062 0.796 0.426 Gendermale 0.266 1.305 0.570 0.427 3.988 0.468 0.640 RaceBlack -0.349 0.706 1.495 0.038 13.209 -0.233 0.816 RaceWhite -1.057 0.347 0.901 0.059 2.034 -1.173 0.241 Stage2 0.679 1.971 0.685 0.515 7.542 0.991 0.322 Stage3 -15.516 0.000 6945.680 0.000 Inf -0.002 0.998 Stage4 0.848 2.336 0.686 0.608 8.969 1.236 0.216 Purity 2.010 7.464 1.569 0.345 161.611 1.281 0.200 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.78e-01 Wald test p = 6.55e-01 Score (logrank) test p = 4.87e-01 IDI1 in COAD (n=458): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.025 0.976 0.216 0.638 1.491 -0.114 0.909 Age 0.024 1.024 0.011 1.001 1.047 2.077 0.038 * Gendermale 0.216 1.241 0.270 0.731 2.105 0.800 0.424 RaceBlack -0.427 0.652 0.834 0.127 3.347 -0.512 0.609 RaceWhite -0.458 0.633 0.785 0.136 2.949 -0.583 0.560 Stage2 0.212 1.236 0.562 0.410 3.720 0.376 0.707 Stage3 0.809 2.245 0.550 0.764 6.592 1.471 0.141 Stage4 1.886 6.593 0.552 2.233 19.470 3.414 0.001 ** Purity -0.231 0.794 0.602 0.244 2.585 -0.383 0.702 Rsquare = 0.109 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.62e-04 Wald test p = 1.58e-04 Score (logrank) test p = 2.33e-05 IDI1 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.538 1.713 1.151 0.179 16.357 0.467 0.640 Age -0.003 0.997 0.042 0.919 1.082 -0.066 0.947 Gendermale 0.737 2.090 1.070 0.256 17.031 0.689 0.491 RaceBlack 0.707 2.027 1.764 0.064 64.293 0.401 0.689 RaceWhite -2.204 0.110 1.326 0.008 1.485 -1.662 0.097 · Purity -1.896 0.150 2.022 0.003 7.909 -0.937 0.349 Rsquare = 0.135 (max possible = 5.58e-01 ) Likelihood ratio test p = 4.28e-01 Wald test p = 5.87e-01 Score (logrank) test p = 3.34e-01 IDI1 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.152 0.859 0.236 0.541 1.366 -0.641 0.521 Age 0.010 1.010 0.014 0.983 1.038 0.704 0.482 Gendermale 0.493 1.638 0.537 0.571 4.697 0.918 0.359 RaceBlack 0.262 1.300 1.074 0.158 10.661 0.244 0.807 RaceWhite -0.067 0.936 0.445 0.391 2.239 -0.150 0.881 Stage2 0.655 1.924 0.655 0.533 6.950 0.999 0.318 Stage3 1.442 4.231 0.668 1.142 15.675 2.158 0.031 * Stage4 2.846 17.215 0.771 3.796 78.072 3.689 0.000 *** Purity 0.333 1.394 0.791 0.296 6.577 0.420 0.674 Rsquare = 0.143 (max possible = 9.32e-01 ) Likelihood ratio test p = 9.95e-03 Wald test p = 4.68e-03 Score (logrank) test p = 3.85e-04 IDI1 in GBM (n=153): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.058 0.944 0.209 0.627 1.420 -0.279 0.781 Age 0.029 1.030 0.008 1.013 1.047 3.478 0.001 ** Gendermale -0.094 0.910 0.213 0.600 1.382 -0.440 0.660 RaceBlack 0.538 1.713 0.727 0.412 7.124 0.740 0.459 RaceWhite -0.237 0.789 0.614 0.237 2.628 -0.387 0.699 Purity -1.055 0.348 0.546 0.119 1.016 -1.930 0.054 · Rsquare = 0.13 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.66e-03 Wald test p = 7.05e-03 Score (logrank) test p = 6e-03 IDI1 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.161 1.175 0.117 0.934 1.477 1.377 0.169 Age 0.021 1.022 0.008 1.006 1.037 2.809 0.005 ** Gendermale -0.236 0.789 0.172 0.564 1.105 -1.378 0.168 RaceBlack 0.065 1.067 0.561 0.355 3.202 0.115 0.908 RaceWhite -0.262 0.769 0.511 0.282 2.095 -0.513 0.608 Stage2 0.644 1.905 0.544 0.656 5.533 1.184 0.236 Stage3 0.884 2.419 0.537 0.844 6.935 1.645 0.100 Stage4 1.289 3.628 0.511 1.333 9.875 2.522 0.012 * Purity -0.113 0.893 0.368 0.434 1.837 -0.307 0.759 Rsquare = 0.074 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.5e-04 Wald test p = 7.07e-04 Score (logrank) test p = 5.12e-04 IDI1 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.011 9.890000e-01 0.420 0.434 2.254 -0.026 0.979 Age 0.011 1.011000e+00 0.026 0.961 1.063 0.417 0.677 Gendermale -0.151 8.590000e-01 0.557 0.288 2.561 -0.272 0.786 RaceBlack 18.890 1.598753e+08 12036.386 0.000 Inf 0.002 0.999 RaceWhite 18.109 7.318997e+07 12036.386 0.000 Inf 0.002 0.999 Stage2 17.455 3.806969e+07 5290.340 0.000 Inf 0.003 0.997 Stage3 16.602 1.622613e+07 5290.340 0.000 Inf 0.003 0.997 Stage4 17.483 3.915242e+07 5290.340 0.000 Inf 0.003 0.997 Purity -1.572 2.080000e-01 1.070 0.025 1.691 -1.469 0.142 Rsquare = 0.087 (max possible = 9.17e-01 ) Likelihood ratio test p = 7.48e-01 Wald test p = 9.54e-01 Score (logrank) test p = 8.67e-01 IDI1 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.152 1.165 0.124 0.913 1.486 1.228 0.219 Age 0.026 1.026 0.008 1.010 1.043 3.084 0.002 ** Gendermale -0.266 0.767 0.183 0.536 1.097 -1.452 0.146 RaceBlack -0.085 0.918 0.567 0.302 2.790 -0.151 0.880 RaceWhite -0.399 0.671 0.513 0.246 1.833 -0.778 0.437 Stage2 0.399 1.490 0.554 0.503 4.418 0.719 0.472 Stage3 0.760 2.137 0.542 0.739 6.181 1.402 0.161 Stage4 1.183 3.264 0.513 1.194 8.924 2.305 0.021 * Purity 0.144 1.154 0.405 0.522 2.552 0.355 0.723 Rsquare = 0.089 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.87e-04 Wald test p = 5.68e-04 Score (logrank) test p = 4.29e-04 IDI1 in KICH (n=66): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p IDI1 -0.409 6.640000e-01 0.475 0.262 1.685000e+00 -0.861 0.389 Age 0.070 1.072000e+00 0.029 1.013 1.134000e+00 2.412 0.016 Gendermale -0.921 3.980000e-01 0.729 0.095 1.663000e+00 -1.263 0.207 RaceBlack -17.479 0.000000e+00 6277.349 0.000 Inf -0.003 0.998 RaceWhite -2.273 1.030000e-01 1.163 0.011 1.006000e+00 -1.955 0.051 Stage2 16.255 1.146896e+07 0.849 2172417.315 6.054872e+07 19.149 0.000 Stage3 17.330 3.358586e+07 0.778 7316373.562 1.541761e+08 22.287 0.000 Stage4 19.913 4.448454e+08 0.903 75854156.305 2.608788e+09 22.064 0.000 Purity 1.753 5.774000e+00 4.008 0.002 1.490264e+04 0.437 0.662 signif IDI1 Age * Gendermale RaceBlack RaceWhite · Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.35 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.35e-03 Wald test p = 1.04e-287 Score (logrank) test p = 9.46e-09 IDI1 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.373 0.689 0.134 0.530 0.896 -2.783 0.005 ** Age 0.033 1.034 0.008 1.017 1.051 3.991 0.000 *** Gendermale -0.136 0.872 0.186 0.606 1.256 -0.734 0.463 RaceBlack 0.342 1.408 1.058 0.177 11.198 0.324 0.746 RaceWhite 0.366 1.442 1.018 0.196 10.603 0.359 0.719 Stage2 0.264 1.303 0.345 0.662 2.563 0.766 0.444 Stage3 0.671 1.956 0.236 1.233 3.103 2.848 0.004 ** Stage4 1.656 5.238 0.219 3.413 8.038 7.578 0.000 *** Purity -0.116 0.890 0.367 0.434 1.827 -0.316 0.752 Rsquare = 0.188 (max possible = 9.65e-01 ) Likelihood ratio test p = 3.29e-16 Wald test p = 1.53e-16 Score (logrank) test p = 6.9e-20 IDI1 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.358 1.430 0.244 0.887 2.307 1.468 0.142 Age 0.009 1.009 0.015 0.979 1.040 0.568 0.570 Gendermale -0.480 0.619 0.383 0.292 1.311 -1.253 0.210 RaceBlack -1.822 0.162 1.206 0.015 1.719 -1.511 0.131 RaceWhite -1.953 0.142 1.188 0.014 1.455 -1.644 0.100 Stage2 -0.358 0.699 1.055 0.088 5.524 -0.340 0.734 Stage3 1.637 5.138 0.427 2.224 11.871 3.830 0.000 *** Stage4 2.906 18.286 0.530 6.476 51.633 5.487 0.000 *** Purity -0.393 0.675 0.739 0.158 2.875 -0.532 0.595 Rsquare = 0.171 (max possible = 7.58e-01 ) Likelihood ratio test p = 4.65e-06 Wald test p = 2.07e-06 Score (logrank) test p = 3.16e-10 IDI1 in LAML (n=173): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.486 1.626 0.184 1.133 2.334 2.637 0.008 ** Age 0.039 1.040 0.008 1.024 1.057 4.815 0.000 *** Gendermale -0.152 0.859 0.211 0.568 1.300 -0.717 0.473 RaceBlack 0.063 1.065 1.115 0.120 9.478 0.057 0.955 RaceWhite -0.648 0.523 1.018 0.071 3.844 -0.637 0.524 Rsquare = 0.194 (max possible = 9.96e-01 ) Likelihood ratio test p = 5.69e-06 Wald test p = 2.34e-05 Score (logrank) test p = 1.64e-05 IDI1 in LGG (n=516): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.767 0.464 0.144 0.350 0.616 -5.323 0.000 *** Age 0.059 1.061 0.008 1.045 1.078 7.461 0.000 *** Gendermale -0.018 0.982 0.195 0.670 1.440 -0.093 0.926 RaceBlack 15.226 4097075.509 2237.415 0.000 Inf 0.007 0.995 RaceWhite 15.321 4505522.325 2237.415 0.000 Inf 0.007 0.995 Purity -0.684 0.505 0.415 0.224 1.139 -1.647 0.100 Rsquare = 0.185 (max possible = 9.07e-01 ) Likelihood ratio test p = 2.93e-18 Wald test p = 9.72e-18 Score (logrank) test p = 5.58e-19 IDI1 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.102 1.107 0.108 0.895 1.369 0.941 0.347 Age 0.011 1.011 0.008 0.995 1.027 1.335 0.182 Gendermale -0.120 0.887 0.228 0.567 1.386 -0.527 0.598 RaceBlack 0.848 2.334 0.492 0.890 6.119 1.724 0.085 · RaceWhite 0.013 1.013 0.237 0.637 1.613 0.056 0.955 Stage2 0.345 1.412 0.263 0.844 2.362 1.313 0.189 Stage3 0.939 2.557 0.235 1.615 4.049 4.003 0.000 *** Stage4 1.570 4.804 0.618 1.431 16.135 2.539 0.011 * Purity 0.508 1.662 0.464 0.669 4.129 1.094 0.274 Rsquare = 0.087 (max possible = 9.66e-01 ) Likelihood ratio test p = 8.52e-04 Wald test p = 4.88e-04 Score (logrank) test p = 1.84e-04 IDI1 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.139 1.149 0.119 0.910 1.451 1.168 0.243 Age 0.007 1.007 0.009 0.989 1.025 0.736 0.462 Gendermale -0.001 0.999 0.170 0.716 1.393 -0.007 0.995 RaceBlack 16.260 11519510.119 1885.425 0.000 Inf 0.009 0.993 RaceWhite 16.421 13532008.693 1885.425 0.000 Inf 0.009 0.993 Stage2 0.856 2.353 0.201 1.587 3.490 4.255 0.000 *** Stage3 1.019 2.771 0.218 1.809 4.246 4.681 0.000 *** Stage4 0.982 2.670 0.335 1.386 5.144 2.936 0.003 ** Purity 0.588 1.801 0.344 0.919 3.531 1.713 0.087 · Rsquare = 0.099 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.34e-06 Wald test p = 2.03e-05 Score (logrank) test p = 2.27e-06 IDI1 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.018 1.018 0.126 0.796 1.303 0.142 0.887 Age 0.016 1.016 0.009 0.998 1.035 1.746 0.081 · Gendermale 0.433 1.542 0.195 1.052 2.259 2.220 0.026 * RaceBlack 0.008 1.008 0.607 0.307 3.311 0.012 0.990 RaceWhite -0.516 0.597 0.564 0.198 1.805 -0.914 0.361 Stage2 0.209 1.232 0.188 0.853 1.780 1.112 0.266 Stage3 0.602 1.827 0.214 1.200 2.781 2.810 0.005 ** Stage4 0.754 2.125 0.797 0.446 10.127 0.946 0.344 Purity -0.347 0.707 0.365 0.345 1.447 -0.950 0.342 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.41e-02 Wald test p = 1.86e-02 Score (logrank) test p = 1.6e-02 IDI1 in MESO (n=87): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.027 1.027 0.223 0.663 1.590 0.119 0.905 Age 0.020 1.020 0.016 0.987 1.053 1.185 0.236 Gendermale -0.186 0.831 0.336 0.430 1.604 -0.553 0.580 RaceBlack 0.122 1.130 1.533 0.056 22.820 0.080 0.937 RaceWhite -0.509 0.601 1.046 0.077 4.667 -0.487 0.626 Stage2 -0.237 0.789 0.469 0.315 1.977 -0.506 0.613 Stage3 -0.106 0.900 0.420 0.395 2.048 -0.252 0.801 Stage4 -0.155 0.856 0.478 0.335 2.186 -0.325 0.745 Purity -0.734 0.480 0.576 0.155 1.484 -1.274 0.203 Rsquare = 0.06 (max possible = 9.98e-01 ) Likelihood ratio test p = 8.1e-01 Wald test p = 7.89e-01 Score (logrank) test p = 7.82e-01 IDI1 in OV (n=303): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.022 1.022 0.102 0.838 1.248 0.219 0.827 Age 0.036 1.037 0.008 1.020 1.053 4.425 0.000 *** RaceBlack -0.048 0.953 0.577 0.308 2.954 -0.083 0.934 RaceWhite -0.161 0.851 0.515 0.310 2.339 -0.312 0.755 Purity -0.570 0.566 0.677 0.150 2.132 -0.842 0.400 Rsquare = 0.081 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.13e-03 Wald test p = 9.78e-04 Score (logrank) test p = 8.09e-04 IDI1 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.099 0.906 0.169 0.651 1.261 -0.587 0.557 Age 0.022 1.022 0.011 1.001 1.044 2.020 0.043 * Gendermale -0.207 0.813 0.216 0.532 1.243 -0.956 0.339 RaceBlack 0.002 1.002 0.739 0.235 4.267 0.003 0.997 RaceWhite 0.364 1.439 0.473 0.569 3.639 0.769 0.442 Stage2 0.603 1.827 0.439 0.773 4.317 1.374 0.169 Stage3 -0.296 0.744 1.096 0.087 6.372 -0.270 0.787 Stage4 0.326 1.386 0.837 0.269 7.149 0.390 0.697 Purity -0.710 0.492 0.413 0.219 1.104 -1.720 0.085 · Rsquare = 0.09 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.51e-02 Wald test p = 1.08e-01 Score (logrank) test p = 1.01e-01 IDI1 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.115 1.122 0.642 0.319 3.953 0.180 0.857 Age 0.037 1.038 0.029 0.981 1.097 1.291 0.197 Gendermale 1.365 3.915 0.917 0.649 23.627 1.488 0.137 RaceBlack -0.344 0.709 19547.442 0.000 Inf 0.000 1.000 RaceWhite 17.227 30305249.077 15734.694 0.000 Inf 0.001 0.999 Purity 5.662 287.753 3.442 0.338 244701.255 1.645 0.100 Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.6e-01 Wald test p = 4.1e-01 Score (logrank) test p = 3.1e-01 IDI1 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.493 1.637 0.476 0.644 4.160 1.036 0.300 Age 0.017 1.017 0.057 0.909 1.138 0.294 0.769 RaceBlack 15.248 4188231.693 6948.763 0.000 Inf 0.002 0.998 RaceWhite 16.464 14133333.620 6948.763 0.000 Inf 0.002 0.998 Purity 1.284 3.610 1.405 0.230 56.730 0.914 0.361 Rsquare = 0.009 (max possible = 1.83e-01 ) Likelihood ratio test p = 5.82e-01 Wald test p = 6.93e-01 Score (logrank) test p = 6.31e-01 IDI1 in READ (n=166): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.135 1.144 0.406 0.516 2.535 0.331 0.740 Age 0.113 1.120 0.046 1.024 1.224 2.483 0.013 * Gendermale -0.437 0.646 0.736 0.153 2.733 -0.594 0.552 RaceBlack 13.207 544074.345 10200.840 0.000 Inf 0.001 0.999 RaceWhite 12.183 195475.228 10200.840 0.000 Inf 0.001 0.999 Stage2 -1.837 0.159 1.257 0.014 1.874 -1.461 0.144 Stage3 -0.392 0.676 0.938 0.107 4.252 -0.418 0.676 Stage4 -0.108 0.898 0.962 0.136 5.921 -0.112 0.911 Purity 0.241 1.272 1.370 0.087 18.653 0.176 0.860 Rsquare = 0.21 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.57e-02 Wald test p = 2.55e-01 Score (logrank) test p = 4.88e-02 IDI1 in SARC (n=260): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.300 1.350 0.173 0.961 1.897 1.731 0.083 · Age 0.021 1.022 0.008 1.005 1.039 2.574 0.010 * Gendermale 0.014 1.014 0.223 0.655 1.569 0.062 0.951 RaceBlack -0.150 0.860 1.086 0.102 7.235 -0.138 0.890 RaceWhite -0.533 0.587 1.024 0.079 4.365 -0.520 0.603 Purity 0.881 2.412 0.574 0.783 7.435 1.533 0.125 Rsquare = 0.055 (max possible = 9.75e-01 ) Likelihood ratio test p = 4.16e-02 Wald test p = 5.62e-02 Score (logrank) test p = 5.81e-02 IDI1 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.103 0.902 0.084 0.765 1.063 -1.229 0.219 Age 0.019 1.019 0.005 1.009 1.029 3.611 0.000 *** Gendermale -0.026 0.974 0.159 0.714 1.329 -0.164 0.870 RaceWhite -1.223 0.294 0.405 0.133 0.651 -3.021 0.003 ** Stage2 0.257 1.293 0.219 0.842 1.986 1.174 0.240 Stage3 0.595 1.813 0.205 1.214 2.707 2.907 0.004 ** Stage4 1.354 3.872 0.352 1.943 7.718 3.847 0.000 *** Purity 1.014 2.756 0.341 1.414 5.374 2.976 0.003 ** Rsquare = 0.127 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.15e-08 Wald test p = 6.26e-09 Score (logrank) test p = 7.65e-10 IDI1 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.108 1.114000e+00 0.195 0.761 1.632 0.555 0.579 Age 0.012 1.012000e+00 0.016 0.981 1.044 0.757 0.449 Gendermale 0.198 1.219000e+00 0.435 0.519 2.861 0.455 0.649 RaceWhite -1.271 2.800000e-01 0.626 0.082 0.957 -2.030 0.042 * Stage2 17.431 3.718017e+07 6217.505 0.000 Inf 0.003 0.998 Stage3 17.960 6.306555e+07 6217.505 0.000 Inf 0.003 0.998 Stage4 19.914 4.452361e+08 6217.505 0.000 Inf 0.003 0.997 Purity 0.038 1.039000e+00 1.032 0.137 7.852 0.037 0.970 Rsquare = 0.149 (max possible = 8.69e-01 ) Likelihood ratio test p = 5.54e-02 Wald test p = 4.98e-02 Score (logrank) test p = 3.98e-03 IDI1 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.154 0.857 0.095 0.711 1.033 -1.623 0.105 Age 0.021 1.021 0.006 1.010 1.033 3.764 0.000 *** Gendermale -0.024 0.977 0.174 0.695 1.372 -0.137 0.891 RaceWhite -0.911 0.402 0.607 0.122 1.321 -1.501 0.133 Stage2 0.124 1.132 0.231 0.720 1.782 0.538 0.591 Stage3 0.543 1.722 0.209 1.142 2.595 2.596 0.009 ** Stage4 1.124 3.078 0.400 1.404 6.745 2.808 0.005 ** Purity 1.116 3.054 0.371 1.475 6.324 3.006 0.003 ** Rsquare = 0.141 (max possible = 9.95e-01 ) Likelihood ratio test p = 3.56e-07 Wald test p = 6.08e-07 Score (logrank) test p = 2.33e-07 IDI1 in STAD (n=415): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.151 0.860 0.138 0.657 1.127 -1.094 0.274 Age 0.028 1.028 0.010 1.008 1.049 2.723 0.006 ** Gendermale 0.157 1.170 0.210 0.776 1.765 0.749 0.454 RaceBlack 0.287 1.332 0.447 0.555 3.200 0.642 0.521 RaceWhite 0.125 1.133 0.246 0.700 1.834 0.510 0.610 Stage2 0.521 1.684 0.391 0.783 3.621 1.334 0.182 Stage3 0.952 2.591 0.364 1.269 5.291 2.613 0.009 ** Stage4 1.358 3.887 0.505 1.446 10.450 2.691 0.007 ** Purity -0.512 0.599 0.379 0.285 1.260 -1.350 0.177 Rsquare = 0.073 (max possible = 9.79e-01 ) Likelihood ratio test p = 8.97e-03 Wald test p = 1.18e-02 Score (logrank) test p = 9.61e-03 IDI1 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 20.799 1.078160e+09 84915.994 0 Inf 0.000 1.000 Age -1.562 2.100000e-01 1692.893 0 Inf -0.001 0.999 RaceBlack 7.680 2.163913e+03 17897599.687 0 Inf 0.000 1.000 RaceWhite -44.077 0.000000e+00 18084396.619 0 Inf 0.000 1.000 Stage2 4.766 1.174470e+02 39542.501 0 Inf 0.000 1.000 Stage3 6.210 4.978860e+02 167201.961 0 Inf 0.000 1.000 Purity 34.946 1.502760e+15 207392.087 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 2.87e-03 IDI1 in THCA (n=509): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 1.108 3.028 0.546 1.038 8.832 2.028 0.043 * Age 0.134 1.143 0.027 1.083 1.206 4.882 0.000 *** Gendermale -0.190 0.827 0.637 0.237 2.882 -0.298 0.766 RaceBlack 17.634 45544420.000 9334.579 0.000 Inf 0.002 0.998 RaceWhite 17.477 38934406.770 9334.579 0.000 Inf 0.002 0.999 Stage2 0.582 1.789 1.099 0.208 15.411 0.529 0.597 Stage3 0.424 1.527 0.867 0.279 8.355 0.489 0.625 Stage4 2.192 8.957 1.010 1.236 64.896 2.170 0.030 * Purity 2.166 8.724 1.096 1.017 74.821 1.976 0.048 * Rsquare = 0.159 (max possible = 3.47e-01 ) Likelihood ratio test p = 3.04e-11 Wald test p = 2.14e-05 Score (logrank) test p = 7.67e-12 IDI1 in THYM (n=120): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.564 0.569 0.582 0.182 1.779 -0.970 0.332 Age 0.049 1.050 0.031 0.988 1.117 1.566 0.117 Gendermale -0.352 0.703 0.761 0.158 3.128 -0.462 0.644 RaceBlack -16.368 0.000 10509.229 0.000 Inf -0.002 0.999 RaceWhite 0.565 1.759 1.098 0.204 15.141 0.514 0.607 Purity 0.520 1.681 1.122 0.187 15.154 0.463 0.643 Rsquare = 0.052 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.24e-01 Wald test p = 5.5e-01 Score (logrank) test p = 4.42e-01 IDI1 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 0.273 1.314 0.185 0.914 1.890 1.475 0.140 Age 0.049 1.050 0.016 1.018 1.083 3.096 0.002 ** RaceBlack -0.422 0.656 0.795 0.138 3.114 -0.531 0.595 RaceWhite -0.542 0.581 0.746 0.135 2.511 -0.726 0.468 Purity 0.577 1.781 0.667 0.482 6.578 0.866 0.386 Rsquare = 0.046 (max possible = 7.81e-01 ) Likelihood ratio test p = 2.14e-02 Wald test p = 2.42e-02 Score (logrank) test p = 2.29e-02 IDI1 in UCS (n=57): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.259 0.772 0.312 0.419 1.422 -0.831 0.406 Age 0.048 1.049 0.025 0.999 1.101 1.930 0.054 · RaceBlack 17.725 49850575.528 6516.625 0.000 Inf 0.003 0.998 RaceWhite 17.821 54924819.026 6516.625 0.000 Inf 0.003 0.998 Purity -0.831 0.436 1.046 0.056 3.387 -0.794 0.427 Rsquare = 0.131 (max possible = 9.83e-01 ) Likelihood ratio test p = 2e-01 Wald test p = 2.9e-01 Score (logrank) test p = 2.08e-01 IDI1 in UVM (n=80): Model: Surv(OS, EVENT) ~ `IDI1` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif IDI1 -0.015 0.985 0.230 0.627 1.548 -0.064 0.949 Age 0.040 1.041 0.019 1.003 1.081 2.100 0.036 * Gendermale 0.272 1.313 0.484 0.508 3.391 0.562 0.574 Stage3 0.280 1.323 0.504 0.492 3.554 0.555 0.579 Stage4 3.754 42.709 1.219 3.917 465.725 3.080 0.002 ** Purity 1.955 7.062 1.232 0.632 78.920 1.587 0.112 Rsquare = 0.253 (max possible = 8.72e-01 ) Likelihood ratio test p = 1.01e-03 Wald test p = 3.49e-03 Score (logrank) test p = 2.52e-09 LSS in ACC (n=79): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.222 1.248 0.179 0.879 1.772 1.240 0.215 Age 0.009 1.009 0.014 0.981 1.038 0.651 0.515 Gendermale 0.558 1.748 0.448 0.727 4.203 1.247 0.212 RaceBlack -0.467 0.627 12186.882 0.000 Inf 0.000 1.000 RaceWhite 16.473 14265136.446 10400.701 0.000 Inf 0.002 0.999 Purity 2.415 11.191 2.296 0.124 1006.684 1.052 0.293 Rsquare = 0.089 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.26e-01 Wald test p = 7.06e-01 Score (logrank) test p = 5.45e-01 LSS in BLCA (n=408): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.185 1.203 0.112 0.966 1.498 1.653 0.098 · Age 0.032 1.033 0.009 1.015 1.050 3.728 0.000 *** Gendermale -0.192 0.825 0.179 0.581 1.171 -1.077 0.282 RaceBlack 0.595 1.812 0.451 0.749 4.387 1.318 0.187 RaceWhite 0.044 1.045 0.356 0.520 2.101 0.123 0.902 Stage2 14.418 1827318.246 1883.617 0.000 Inf 0.008 0.994 Stage3 14.900 2957796.409 1883.617 0.000 Inf 0.008 0.994 Stage4 15.398 4866269.413 1883.617 0.000 Inf 0.008 0.993 Purity 0.132 1.142 0.339 0.588 2.217 0.391 0.696 Rsquare = 0.137 (max possible = 9.91e-01 ) Likelihood ratio test p = 6.11e-08 Wald test p = 3.52e-07 Score (logrank) test p = 1e-07 LSS in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.354 1.424 0.129 1.106 1.834 2.745 0.006 ** Age 0.036 1.037 0.008 1.022 1.053 4.843 0.000 *** Gendermale 0.099 1.105 1.008 0.153 7.969 0.099 0.921 RaceBlack -0.123 0.884 0.620 0.262 2.978 -0.199 0.842 RaceWhite -0.319 0.727 0.596 0.226 2.338 -0.535 0.592 Stage2 0.418 1.519 0.304 0.838 2.754 1.377 0.169 Stage3 1.180 3.256 0.313 1.763 6.011 3.773 0.000 *** Stage4 2.552 12.836 0.389 5.989 27.513 6.561 0.000 *** Purity 0.504 1.655 0.422 0.723 3.785 1.193 0.233 Rsquare = 0.088 (max possible = 7.85e-01 ) Likelihood ratio test p = 9.35e-14 Wald test p = 6.74e-17 Score (logrank) test p = 3.41e-23 LSS in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.487 1.627000e+00 0.293 0.917 2.889 1.663 0.096 · Age 0.012 1.012000e+00 0.018 0.977 1.048 0.647 0.517 RaceBlack -1.052 3.490000e-01 1.110 0.040 3.079 -0.947 0.344 RaceWhite -1.252 2.860000e-01 1.105 0.033 2.496 -1.132 0.257 Stage2 18.743 1.379962e+08 6435.700 0.000 Inf 0.003 0.998 Stage3 20.193 5.881802e+08 6435.700 0.000 Inf 0.003 0.997 Stage4 21.360 1.891107e+09 6435.700 0.000 Inf 0.003 0.997 Purity 0.872 2.391000e+00 0.983 0.348 16.411 0.887 0.375 Rsquare = 0.171 (max possible = 7.18e-01 ) Likelihood ratio test p = 1.68e-04 Wald test p = 3.54e-03 Score (logrank) test p = 1.73e-06 LSS in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.848 2.336000e+00 0.731 0.558 9.786 1.161 0.246 Age 0.025 1.025000e+00 0.028 0.970 1.083 0.892 0.372 RaceBlack -3.413 3.300000e-02 1.836 0.001 1.203 -1.859 0.063 · RaceWhite -2.033 1.310000e-01 1.487 0.007 2.413 -1.367 0.172 Stage2 18.601 1.197686e+08 14224.060 0.000 Inf 0.001 0.999 Stage3 20.349 6.880773e+08 14224.060 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.741 4.214400e+01 2.332 0.436 4069.557 1.604 0.109 Rsquare = 0.385 (max possible = 6.68e-01 ) Likelihood ratio test p = 1.67e-04 Wald test p = 1.94e-01 Score (logrank) test p = 8.39e-15 LSS in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.200 1.222 0.232 0.776 1.923 0.864 0.388 Age 0.049 1.050 0.012 1.026 1.074 4.108 0.000 *** Gendermale -15.303 0.000 3465.067 0.000 Inf -0.004 0.996 RaceBlack -0.499 0.607 1.177 0.060 6.090 -0.424 0.671 RaceWhite 0.145 1.156 1.039 0.151 8.852 0.139 0.889 Stage2 0.335 1.398 0.374 0.672 2.910 0.896 0.370 Stage3 0.829 2.290 0.395 1.056 4.968 2.098 0.036 * Stage4 2.129 8.406 0.592 2.634 26.820 3.596 0.000 *** Purity 0.261 1.298 0.619 0.386 4.363 0.421 0.673 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 7.74e-05 Wald test p = 1.52e-05 Score (logrank) test p = 2.82e-07 LSS in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.217 1.242 0.256 0.752 2.051 0.846 0.397 Age 0.050 1.051 0.021 1.010 1.095 2.418 0.016 * Gendermale 0.989 2.689 1.105 0.308 23.463 0.895 0.371 RaceBlack 16.785 19490039.807 6324.287 0.000 Inf 0.003 0.998 RaceWhite 16.162 10451841.294 6324.287 0.000 Inf 0.003 0.998 Stage2 0.586 1.797 1.082 0.216 14.976 0.542 0.588 Stage3 1.540 4.665 1.062 0.582 37.406 1.450 0.147 Stage4 2.100 8.169 1.174 0.818 81.602 1.789 0.074 · Purity 1.060 2.886 1.314 0.219 37.947 0.806 0.420 Rsquare = 0.109 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.35e-02 Wald test p = 5.9e-02 Score (logrank) test p = 1.9e-02 LSS in CESC (n=306): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.177 1.194 0.176 0.846 1.686 1.008 0.313 Age 0.010 1.010 0.010 0.991 1.030 1.035 0.301 RaceBlack 1.032 2.806 1.068 0.346 22.753 0.966 0.334 RaceWhite 0.805 2.236 1.015 0.306 16.349 0.793 0.428 Purity 0.523 1.687 0.739 0.396 7.180 0.707 0.479 Rsquare = 0.018 (max possible = 8.91e-01 ) Likelihood ratio test p = 5.27e-01 Wald test p = 5.59e-01 Score (logrank) test p = 5.48e-01 LSS in CHOL (n=36): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.053 0.948 0.399 0.434 2.073 -0.133 0.894 Age 0.018 1.018 0.022 0.976 1.062 0.824 0.410 Gendermale 0.251 1.285 0.562 0.427 3.868 0.446 0.656 RaceBlack -0.364 0.695 1.491 0.037 12.920 -0.244 0.807 RaceWhite -1.083 0.338 0.894 0.059 1.951 -1.212 0.225 Stage2 0.669 1.951 0.673 0.522 7.292 0.994 0.320 Stage3 -15.578 0.000 6943.552 0.000 Inf -0.002 0.998 Stage4 0.856 2.354 0.711 0.584 9.491 1.204 0.229 Purity 1.994 7.347 1.586 0.328 164.557 1.257 0.209 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.79e-01 Wald test p = 6.52e-01 Score (logrank) test p = 4.86e-01 LSS in COAD (n=458): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.059 1.061 0.189 0.732 1.537 0.312 0.755 Age 0.024 1.024 0.011 1.001 1.047 2.057 0.040 * Gendermale 0.224 1.252 0.271 0.736 2.128 0.828 0.408 RaceBlack -0.413 0.662 0.828 0.131 3.353 -0.499 0.618 RaceWhite -0.427 0.653 0.777 0.142 2.992 -0.549 0.583 Stage2 0.209 1.233 0.562 0.410 3.711 0.373 0.710 Stage3 0.804 2.235 0.550 0.761 6.563 1.463 0.143 Stage4 1.878 6.539 0.553 2.211 19.339 3.394 0.001 ** Purity -0.239 0.788 0.604 0.241 2.574 -0.395 0.693 Rsquare = 0.11 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.47e-04 Wald test p = 1.56e-04 Score (logrank) test p = 2.33e-05 LSS in DLBC (n=48): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 1.200 3.319 0.940 0.526 20.930 1.277 0.202 Age -0.007 0.993 0.041 0.916 1.076 -0.173 0.863 Gendermale 0.795 2.214 1.165 0.226 21.721 0.682 0.495 RaceBlack 1.007 2.739 1.741 0.090 83.055 0.579 0.563 RaceWhite -2.814 0.060 1.566 0.003 1.289 -1.798 0.072 · Purity -3.628 0.027 2.793 0.000 6.341 -1.299 0.194 Rsquare = 0.169 (max possible = 5.58e-01 ) Likelihood ratio test p = 2.71e-01 Wald test p = 5.61e-01 Score (logrank) test p = 2.66e-01 LSS in ESCA (n=185): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.340 0.712 0.251 0.435 1.163 -1.357 0.175 Age 0.009 1.009 0.014 0.981 1.037 0.616 0.538 Gendermale 0.430 1.537 0.540 0.534 4.429 0.797 0.426 RaceBlack 0.336 1.400 1.068 0.172 11.362 0.315 0.753 RaceWhite -0.168 0.846 0.448 0.351 2.037 -0.374 0.709 Stage2 0.679 1.971 0.657 0.544 7.143 1.033 0.301 Stage3 1.403 4.068 0.675 1.084 15.275 2.079 0.038 * Stage4 2.761 15.812 0.774 3.472 72.019 3.569 0.000 *** Purity 0.260 1.297 0.779 0.282 5.965 0.334 0.739 Rsquare = 0.152 (max possible = 9.32e-01 ) Likelihood ratio test p = 5.94e-03 Wald test p = 2.95e-03 Score (logrank) test p = 2.26e-04 LSS in GBM (n=153): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.062 0.940 0.134 0.724 1.221 -0.462 0.644 Age 0.030 1.031 0.008 1.014 1.048 3.599 0.000 *** Gendermale -0.093 0.911 0.213 0.600 1.383 -0.437 0.662 RaceBlack 0.483 1.620 0.733 0.385 6.818 0.658 0.510 RaceWhite -0.273 0.761 0.619 0.226 2.559 -0.441 0.659 Purity -1.014 0.363 0.560 0.121 1.086 -1.812 0.070 · Rsquare = 0.13 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.41e-03 Wald test p = 6.77e-03 Score (logrank) test p = 5.81e-03 LSS in HNSC (n=522): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.053 0.948 0.120 0.750 1.200 -0.443 0.658 Age 0.022 1.023 0.008 1.007 1.038 2.919 0.004 ** Gendermale -0.251 0.778 0.172 0.556 1.089 -1.463 0.144 RaceBlack 0.140 1.150 0.559 0.385 3.439 0.250 0.802 RaceWhite -0.249 0.780 0.511 0.286 2.122 -0.487 0.626 Stage2 0.613 1.845 0.544 0.636 5.355 1.127 0.260 Stage3 0.838 2.311 0.537 0.806 6.627 1.558 0.119 Stage4 1.247 3.481 0.510 1.281 9.463 2.445 0.014 * Purity -0.030 0.971 0.365 0.475 1.983 -0.082 0.935 Rsquare = 0.07 (max possible = 9.89e-01 ) Likelihood ratio test p = 4.88e-04 Wald test p = 1.31e-03 Score (logrank) test p = 9.65e-04 LSS in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.550 5.770000e-01 0.437 0.245 1.358 -1.259 0.208 Age 0.014 1.014000e+00 0.026 0.964 1.066 0.535 0.593 Gendermale -0.112 8.940000e-01 0.539 0.311 2.571 -0.207 0.836 RaceBlack 19.212 2.206962e+08 12236.119 0.000 Inf 0.002 0.999 RaceWhite 18.123 7.421918e+07 12236.119 0.000 Inf 0.001 0.999 Stage2 17.540 4.146865e+07 5453.283 0.000 Inf 0.003 0.997 Stage3 16.559 1.554533e+07 5453.283 0.000 Inf 0.003 0.998 Stage4 17.277 3.187959e+07 5453.283 0.000 Inf 0.003 0.997 Purity -1.205 3.000000e-01 1.098 0.035 2.577 -1.097 0.272 Rsquare = 0.11 (max possible = 9.17e-01 ) Likelihood ratio test p = 5.76e-01 Wald test p = 8.53e-01 Score (logrank) test p = 7.18e-01 LSS in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.014 0.986 0.128 0.768 1.265 -0.114 0.910 Age 0.027 1.027 0.008 1.010 1.044 3.194 0.001 ** Gendermale -0.286 0.751 0.183 0.525 1.075 -1.564 0.118 RaceBlack -0.018 0.982 0.564 0.325 2.967 -0.032 0.975 RaceWhite -0.397 0.672 0.512 0.246 1.835 -0.775 0.438 Stage2 0.365 1.441 0.554 0.487 4.266 0.660 0.509 Stage3 0.723 2.061 0.542 0.713 5.960 1.335 0.182 Stage4 1.146 3.147 0.512 1.153 8.583 2.239 0.025 * Purity 0.213 1.237 0.401 0.564 2.713 0.531 0.595 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.39e-04 Wald test p = 9.47e-04 Score (logrank) test p = 7.17e-04 LSS in KICH (n=66): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p LSS 0.436 1.546000e+00 0.608 0.469 5.094000e+00 0.716 0.474 Age 0.075 1.078000e+00 0.029 1.018 1.141000e+00 2.594 0.009 Gendermale -1.004 3.670000e-01 0.731 0.087 1.535000e+00 -1.373 0.170 RaceBlack -17.062 0.000000e+00 6267.576 0.000 Inf -0.003 0.998 RaceWhite -1.879 1.530000e-01 1.167 0.015 1.504000e+00 -1.610 0.107 Stage2 16.010 8.976414e+06 0.849 1699610.833 4.740850e+07 18.855 0.000 Stage3 17.262 3.138063e+07 0.780 6807458.926 1.446566e+08 22.139 0.000 Stage4 19.471 2.857776e+08 0.909 48125142.400 1.697010e+09 21.422 0.000 Purity 1.010 2.746000e+00 3.905 0.001 5.788297e+03 0.259 0.796 signif LSS Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.35 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.31e-03 Wald test p = 9.02e-278 Score (logrank) test p = 7.91e-09 LSS in KIRC (n=533): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.004 1.004 0.184 0.700 1.440 0.022 0.982 Age 0.035 1.036 0.008 1.019 1.053 4.131 0.000 *** Gendermale -0.081 0.922 0.191 0.635 1.341 -0.423 0.672 RaceBlack 0.202 1.223 1.058 0.154 9.733 0.191 0.849 RaceWhite 0.151 1.163 1.016 0.159 8.524 0.149 0.882 Stage2 0.215 1.240 0.345 0.631 2.439 0.623 0.533 Stage3 0.812 2.252 0.232 1.430 3.548 3.501 0.000 *** Stage4 1.760 5.814 0.220 3.775 8.953 7.990 0.000 *** Purity -0.004 0.996 0.369 0.483 2.053 -0.012 0.990 Rsquare = 0.174 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.04e-14 Wald test p = 1.07e-14 Score (logrank) test p = 5.68e-18 LSS in KIRP (n=290): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.354 0.702 0.336 0.364 1.355 -1.054 0.292 Age 0.003 1.003 0.016 0.971 1.035 0.156 0.876 Gendermale -0.505 0.603 0.384 0.284 1.282 -1.315 0.189 RaceBlack -1.887 0.152 1.194 0.015 1.572 -1.581 0.114 RaceWhite -1.899 0.150 1.176 0.015 1.501 -1.615 0.106 Stage2 -0.347 0.707 1.058 0.089 5.617 -0.328 0.743 Stage3 1.662 5.271 0.427 2.283 12.166 3.894 0.000 *** Stage4 2.631 13.889 0.512 5.091 37.892 5.138 0.000 *** Purity -0.247 0.781 0.755 0.178 3.431 -0.327 0.744 Rsquare = 0.167 (max possible = 7.58e-01 ) Likelihood ratio test p = 7.16e-06 Wald test p = 1.81e-06 Score (logrank) test p = 2.87e-10 LSS in LAML (n=173): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.566 1.762 0.163 1.281 2.424 3.480 0.001 ** Age 0.042 1.042 0.008 1.026 1.059 5.093 0.000 *** Gendermale -0.214 0.807 0.213 0.531 1.226 -1.004 0.315 RaceBlack -0.457 0.633 1.107 0.072 5.542 -0.413 0.680 RaceWhite -0.733 0.481 1.019 0.065 3.538 -0.719 0.472 Rsquare = 0.219 (max possible = 9.96e-01 ) Likelihood ratio test p = 6.43e-07 Wald test p = 1.3e-06 Score (logrank) test p = 1.06e-06 LSS in LGG (n=516): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.358 0.699 0.182 0.489 0.999 -1.964 0.050 · Age 0.062 1.064 0.008 1.048 1.080 8.139 0.000 *** Gendermale 0.041 1.041 0.196 0.709 1.529 0.207 0.836 RaceBlack 15.401 4883350.902 2218.435 0.000 Inf 0.007 0.994 RaceWhite 15.406 4904472.939 2218.435 0.000 Inf 0.007 0.994 Purity -0.947 0.388 0.403 0.176 0.855 -2.347 0.019 * Rsquare = 0.144 (max possible = 9.07e-01 ) Likelihood ratio test p = 1.79e-13 Wald test p = 9.08e-14 Score (logrank) test p = 3.89e-15 LSS in LIHC (n=371): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.135 1.144 0.111 0.920 1.422 1.212 0.226 Age 0.010 1.010 0.008 0.994 1.027 1.258 0.208 Gendermale -0.112 0.894 0.228 0.571 1.398 -0.491 0.623 RaceBlack 0.837 2.310 0.492 0.881 6.056 1.703 0.089 · RaceWhite -0.019 0.981 0.238 0.615 1.565 -0.079 0.937 Stage2 0.324 1.382 0.261 0.829 2.305 1.241 0.214 Stage3 0.919 2.508 0.236 1.581 3.979 3.904 0.000 *** Stage4 1.650 5.208 0.620 1.544 17.562 2.661 0.008 ** Purity 0.503 1.654 0.458 0.674 4.062 1.098 0.272 Rsquare = 0.089 (max possible = 9.66e-01 ) Likelihood ratio test p = 6.81e-04 Wald test p = 3.46e-04 Score (logrank) test p = 1.38e-04 LSS in LUAD (n=515): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.123 0.884 0.112 0.709 1.102 -1.094 0.274 Age 0.007 1.007 0.009 0.989 1.025 0.796 0.426 Gendermale 0.009 1.009 0.169 0.725 1.406 0.055 0.956 RaceBlack 16.017 9038956.185 1897.470 0.000 Inf 0.008 0.993 RaceWhite 16.191 10751915.146 1897.470 0.000 Inf 0.009 0.993 Stage2 0.858 2.358 0.201 1.590 3.496 4.269 0.000 *** Stage3 1.036 2.818 0.219 1.835 4.328 4.733 0.000 *** Stage4 1.002 2.725 0.334 1.416 5.245 3.000 0.003 ** Purity 0.625 1.868 0.346 0.949 3.680 1.808 0.071 · Rsquare = 0.099 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.45e-06 Wald test p = 1.89e-05 Score (logrank) test p = 2.06e-06 LSS in LUSC (n=501): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.073 1.076 0.110 0.866 1.335 0.661 0.509 Age 0.016 1.016 0.009 0.998 1.035 1.743 0.081 · Gendermale 0.431 1.538 0.193 1.053 2.246 2.229 0.026 * RaceBlack -0.008 0.992 0.607 0.302 3.259 -0.013 0.990 RaceWhite -0.541 0.582 0.564 0.193 1.758 -0.960 0.337 Stage2 0.200 1.221 0.187 0.846 1.764 1.067 0.286 Stage3 0.590 1.804 0.215 1.183 2.750 2.741 0.006 ** Stage4 0.788 2.199 0.795 0.463 10.455 0.991 0.322 Purity -0.369 0.691 0.367 0.337 1.420 -1.005 0.315 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.09e-02 Wald test p = 1.6e-02 Score (logrank) test p = 1.37e-02 LSS in MESO (n=87): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.176 1.192 0.201 0.804 1.769 0.874 0.382 Age 0.020 1.021 0.016 0.989 1.053 1.289 0.197 Gendermale -0.202 0.817 0.331 0.427 1.563 -0.612 0.541 RaceBlack -0.075 0.928 1.550 0.045 19.345 -0.048 0.962 RaceWhite -0.634 0.531 1.057 0.067 4.212 -0.599 0.549 Stage2 -0.243 0.785 0.468 0.314 1.962 -0.519 0.604 Stage3 -0.089 0.915 0.419 0.403 2.080 -0.212 0.832 Stage4 -0.159 0.853 0.478 0.334 2.180 -0.331 0.740 Purity -0.705 0.494 0.558 0.166 1.475 -1.263 0.207 Rsquare = 0.068 (max possible = 9.98e-01 ) Likelihood ratio test p = 7.37e-01 Wald test p = 7.24e-01 Score (logrank) test p = 7.12e-01 LSS in OV (n=303): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.106 1.111 0.114 0.888 1.391 0.922 0.357 Age 0.036 1.037 0.008 1.020 1.053 4.424 0.000 *** RaceBlack -0.045 0.956 0.577 0.309 2.960 -0.078 0.938 RaceWhite -0.145 0.865 0.516 0.315 2.377 -0.281 0.779 Purity -0.491 0.612 0.669 0.165 2.269 -0.735 0.462 Rsquare = 0.085 (max possible = 9.97e-01 ) Likelihood ratio test p = 8.02e-04 Wald test p = 7.45e-04 Score (logrank) test p = 5.98e-04 LSS in PAAD (n=179): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.365 0.694 0.174 0.493 0.977 -2.094 0.036 * Age 0.021 1.021 0.011 0.999 1.043 1.904 0.057 · Gendermale -0.222 0.801 0.216 0.524 1.224 -1.026 0.305 RaceBlack 0.147 1.159 0.747 0.268 5.009 0.197 0.844 RaceWhite 0.520 1.681 0.484 0.652 4.337 1.075 0.283 Stage2 0.406 1.501 0.446 0.626 3.598 0.911 0.362 Stage3 -0.636 0.529 1.104 0.061 4.609 -0.576 0.565 Stage4 0.226 1.253 0.823 0.250 6.293 0.274 0.784 Purity -0.798 0.450 0.414 0.200 1.014 -1.926 0.054 · Rsquare = 0.113 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.93e-02 Wald test p = 3.06e-02 Score (logrank) test p = 2.82e-02 LSS in PCPG (n=181): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 2.042 7.706 0.891 1.344 44.190 2.292 0.022 * Age 0.047 1.048 0.029 0.990 1.110 1.614 0.107 Gendermale 1.505 4.503 0.884 0.796 25.461 1.702 0.089 · RaceBlack -1.301 0.272 29073.024 0.000 Inf 0.000 1.000 RaceWhite 17.409 36366683.633 23567.824 0.000 Inf 0.001 0.999 Purity 7.840 2539.480 4.223 0.646 9989760.283 1.856 0.063 · Rsquare = 0.088 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.99e-02 Wald test p = 2.81e-01 Score (logrank) test p = 1.04e-01 LSS in PRAD (n=498): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.776 2.173 0.495 0.823 5.735 1.567 0.117 Age 0.013 1.013 0.058 0.904 1.136 0.226 0.821 RaceBlack 15.514 5465449.017 7150.798 0.000 Inf 0.002 0.998 RaceWhite 16.501 14663328.393 7150.798 0.000 Inf 0.002 0.998 Purity 1.085 2.960 1.406 0.188 46.605 0.772 0.440 Rsquare = 0.013 (max possible = 1.83e-01 ) Likelihood ratio test p = 3.95e-01 Wald test p = 4.64e-01 Score (logrank) test p = 3.78e-01 LSS in READ (n=166): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 1.055 2.872 0.638 0.823 10.026 1.654 0.098 · Age 0.170 1.186 0.065 1.045 1.346 2.636 0.008 ** Gendermale -0.386 0.680 0.671 0.182 2.531 -0.576 0.565 RaceBlack 11.269 78353.384 10622.617 0.000 Inf 0.001 0.999 RaceWhite 10.059 23371.902 10622.617 0.000 Inf 0.001 0.999 Stage2 -2.265 0.104 1.353 0.007 1.473 -1.674 0.094 · Stage3 -0.183 0.833 0.914 0.139 4.998 -0.200 0.842 Stage4 0.339 1.404 1.002 0.197 10.008 0.338 0.735 Purity 1.915 6.788 1.795 0.201 228.753 1.067 0.286 Rsquare = 0.24 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.33e-02 Wald test p = 3.21e-01 Score (logrank) test p = 4.52e-02 LSS in SARC (n=260): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.178 1.195 0.148 0.894 1.597 1.205 0.228 Age 0.023 1.023 0.008 1.007 1.040 2.784 0.005 ** Gendermale 0.012 1.012 0.223 0.653 1.568 0.054 0.957 RaceBlack -0.139 0.870 1.086 0.104 7.317 -0.128 0.898 RaceWhite -0.440 0.644 1.023 0.087 4.784 -0.430 0.667 Purity 0.797 2.218 0.586 0.703 7.001 1.359 0.174 Rsquare = 0.049 (max possible = 9.75e-01 ) Likelihood ratio test p = 7.19e-02 Wald test p = 1.01e-01 Score (logrank) test p = 9.64e-02 LSS in SKCM (n=471): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.100 1.106 0.101 0.907 1.347 0.995 0.320 Age 0.018 1.018 0.005 1.008 1.028 3.388 0.001 ** Gendermale -0.058 0.944 0.157 0.693 1.285 -0.365 0.715 RaceWhite -1.279 0.278 0.401 0.127 0.611 -3.187 0.001 ** Stage2 0.273 1.314 0.218 0.857 2.015 1.252 0.211 Stage3 0.619 1.857 0.204 1.245 2.770 3.037 0.002 ** Stage4 1.390 4.015 0.354 2.005 8.042 3.922 0.000 *** Purity 1.010 2.747 0.339 1.413 5.340 2.979 0.003 ** Rsquare = 0.126 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.44e-08 Wald test p = 9.05e-09 Score (logrank) test p = 1.01e-09 LSS in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.032 1.032000e+00 0.357 0.513 2.078 0.089 0.929 Age 0.012 1.012000e+00 0.016 0.981 1.044 0.746 0.456 Gendermale 0.222 1.249000e+00 0.447 0.520 2.998 0.498 0.619 RaceWhite -1.256 2.850000e-01 0.628 0.083 0.975 -2.000 0.045 * Stage2 17.444 3.765787e+07 6199.812 0.000 Inf 0.003 0.998 Stage3 17.954 6.271096e+07 6199.812 0.000 Inf 0.003 0.998 Stage4 20.052 5.111123e+08 6199.812 0.000 Inf 0.003 0.997 Purity 0.272 1.313000e+00 0.947 0.205 8.398 0.287 0.774 Rsquare = 0.147 (max possible = 8.69e-01 ) Likelihood ratio test p = 6.12e-02 Wald test p = 5.49e-02 Score (logrank) test p = 4.56e-03 LSS in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.060 1.062 0.108 0.858 1.313 0.552 0.581 Age 0.020 1.020 0.006 1.009 1.032 3.550 0.000 *** Gendermale -0.064 0.938 0.172 0.669 1.315 -0.373 0.709 RaceWhite -1.061 0.346 0.600 0.107 1.121 -1.769 0.077 · Stage2 0.152 1.164 0.230 0.741 1.827 0.658 0.510 Stage3 0.567 1.764 0.209 1.171 2.656 2.717 0.007 ** Stage4 1.161 3.192 0.403 1.449 7.032 2.880 0.004 ** Purity 1.135 3.110 0.370 1.507 6.417 3.070 0.002 ** Rsquare = 0.135 (max possible = 9.95e-01 ) Likelihood ratio test p = 9.66e-07 Wald test p = 1.64e-06 Score (logrank) test p = 6.1e-07 LSS in STAD (n=415): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.036 1.037 0.153 0.768 1.400 0.238 0.812 Age 0.026 1.027 0.010 1.006 1.048 2.592 0.010 * Gendermale 0.116 1.123 0.209 0.745 1.691 0.553 0.580 RaceBlack 0.259 1.296 0.448 0.538 3.121 0.578 0.563 RaceWhite 0.087 1.091 0.246 0.673 1.766 0.353 0.724 Stage2 0.494 1.639 0.391 0.762 3.524 1.264 0.206 Stage3 0.922 2.514 0.364 1.232 5.127 2.535 0.011 * Stage4 1.329 3.776 0.505 1.404 10.156 2.632 0.008 ** Purity -0.559 0.572 0.380 0.271 1.204 -1.471 0.141 Rsquare = 0.07 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.35e-02 Wald test p = 1.84e-02 Score (logrank) test p = 1.5e-02 LSS in TGCT (n=150): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 7.477 1766.410 50565.215 0 Inf 0.000 1.000 Age -1.552 0.212 1796.283 0 Inf -0.001 0.999 RaceBlack 0.162 1.176 19753280.588 0 Inf 0.000 1.000 RaceWhite -36.745 0.000 19923955.547 0 Inf 0.000 1.000 Stage2 -0.887 0.412 42097.356 0 Inf 0.000 1.000 Stage3 16.930 22515152.254 111678.682 0 Inf 0.000 1.000 Purity 3.746 42.341 234331.825 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 2.96e-03 LSS in THCA (n=509): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.836 2.307 0.753 0.527 10.101 1.109 0.267 Age 0.150 1.162 0.028 1.100 1.228 5.330 0.000 *** Gendermale -0.264 0.768 0.650 0.215 2.749 -0.405 0.685 RaceBlack 17.594 43749966.393 9258.126 0.000 Inf 0.002 0.998 RaceWhite 17.505 40028751.657 9258.126 0.000 Inf 0.002 0.998 Stage2 0.297 1.346 1.139 0.144 12.560 0.261 0.794 Stage3 0.372 1.451 0.850 0.274 7.682 0.438 0.662 Stage4 1.876 6.530 0.995 0.928 45.942 1.885 0.059 · Purity 2.182 8.864 1.071 1.087 72.295 2.038 0.042 * Rsquare = 0.152 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.42e-10 Wald test p = 1.77e-04 Score (logrank) test p = 1.1e-10 LSS in THYM (n=120): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.544 0.580 0.628 0.170 1.985 -0.867 0.386 Age 0.055 1.057 0.033 0.991 1.128 1.679 0.093 · Gendermale -0.083 0.920 0.755 0.209 4.046 -0.110 0.913 RaceBlack -16.283 0.000 9947.590 0.000 Inf -0.002 0.999 RaceWhite 0.664 1.943 1.118 0.217 17.377 0.594 0.552 Purity 0.169 1.184 1.134 0.128 10.936 0.149 0.882 Rsquare = 0.051 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.39e-01 Wald test p = 5.82e-01 Score (logrank) test p = 4.7e-01 LSS in UCEC (n=545): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS 0.135 1.144 0.250 0.701 1.867 0.538 0.590 Age 0.049 1.051 0.016 1.018 1.084 3.110 0.002 ** RaceBlack -0.404 0.668 0.794 0.141 3.166 -0.508 0.611 RaceWhite -0.517 0.597 0.745 0.139 2.570 -0.693 0.488 Purity 0.442 1.556 0.646 0.438 5.521 0.684 0.494 Rsquare = 0.039 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.5e-02 Wald test p = 5.63e-02 Score (logrank) test p = 5.39e-02 LSS in UCS (n=57): Model: Surv(OS, EVENT) ~ `LSS` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.270 0.764 0.273 0.447 1.304 -0.988 0.323 Age 0.047 1.048 0.024 1.000 1.099 1.964 0.050 · RaceBlack 17.985 64692513.011 6539.051 0.000 Inf 0.003 0.998 RaceWhite 18.130 74757109.777 6539.051 0.000 Inf 0.003 0.998 Purity -0.827 0.437 1.042 0.057 3.369 -0.794 0.427 Rsquare = 0.135 (max possible = 9.83e-01 ) Likelihood ratio test p = 1.82e-01 Wald test p = 2.72e-01 Score (logrank) test p = 1.89e-01 LSS in UVM (n=80): Model: Surv(OS, EVENT) ~ `LSS` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif LSS -0.130 0.878 0.524 0.315 2.450 -0.249 0.804 Age 0.041 1.042 0.019 1.003 1.082 2.107 0.035 * Gendermale 0.247 1.280 0.495 0.485 3.375 0.499 0.618 Stage3 0.296 1.345 0.505 0.500 3.622 0.586 0.558 Stage4 3.756 42.788 1.218 3.935 465.250 3.085 0.002 ** Purity 1.930 6.889 1.234 0.613 77.427 1.563 0.118 Rsquare = 0.253 (max possible = 8.72e-01 ) Likelihood ratio test p = 9.82e-04 Wald test p = 3.36e-03 Score (logrank) test p = 2.48e-09 MVD in ACC (n=79): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.386 1.471 0.165 1.065 2.032 2.342 0.019 * Age 0.019 1.019 0.015 0.989 1.050 1.222 0.222 Gendermale 0.791 2.206 0.470 0.878 5.543 1.684 0.092 · RaceBlack -0.958 0.384 12688.994 0.000 Inf 0.000 1.000 RaceWhite 15.603 5973012.120 10844.045 0.000 Inf 0.001 0.999 Purity 2.024 7.565 2.361 0.074 774.188 0.857 0.391 Rsquare = 0.159 (max possible = 9.38e-01 ) Likelihood ratio test p = 8.68e-02 Wald test p = 2.88e-01 Score (logrank) test p = 1.65e-01 MVD in BLCA (n=408): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.146 1.158 0.124 0.909 1.475 1.185 0.236 Age 0.033 1.033 0.009 1.016 1.051 3.815 0.000 *** Gendermale -0.181 0.835 0.179 0.588 1.185 -1.011 0.312 RaceBlack 0.695 2.003 0.446 0.836 4.799 1.558 0.119 RaceWhite 0.120 1.127 0.354 0.563 2.255 0.338 0.735 Stage2 14.468 1920266.684 1863.176 0.000 Inf 0.008 0.994 Stage3 14.909 2985466.330 1863.176 0.000 Inf 0.008 0.994 Stage4 15.446 5105198.562 1863.176 0.000 Inf 0.008 0.993 Purity 0.140 1.150 0.340 0.590 2.240 0.411 0.681 Rsquare = 0.134 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.11e-07 Wald test p = 7.94e-07 Score (logrank) test p = 2.13e-07 MVD in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.124 1.132 0.111 0.911 1.406 1.119 0.263 Age 0.036 1.037 0.008 1.022 1.053 4.820 0.000 *** Gendermale 0.018 1.018 1.007 0.141 7.329 0.018 0.986 RaceBlack -0.056 0.946 0.621 0.280 3.193 -0.090 0.929 RaceWhite -0.207 0.813 0.596 0.253 2.614 -0.347 0.729 Stage2 0.396 1.486 0.304 0.819 2.697 1.303 0.193 Stage3 1.154 3.171 0.314 1.713 5.870 3.673 0.000 *** Stage4 2.428 11.333 0.396 5.210 24.651 6.123 0.000 *** Purity 0.550 1.733 0.424 0.755 3.975 1.297 0.195 Rsquare = 0.082 (max possible = 7.85e-01 ) Likelihood ratio test p = 1.4e-12 Wald test p = 3.07e-16 Score (logrank) test p = 3.19e-22 MVD in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.137 1.146000e+00 0.257 0.693 1.896 0.532 0.595 Age 0.012 1.012000e+00 0.018 0.977 1.048 0.662 0.508 RaceBlack -0.981 3.750000e-01 1.120 0.042 3.372 -0.875 0.381 RaceWhite -1.225 2.940000e-01 1.126 0.032 2.669 -1.088 0.277 Stage2 18.706 1.329674e+08 6451.493 0.000 Inf 0.003 0.998 Stage3 20.120 5.469290e+08 6451.493 0.000 Inf 0.003 0.998 Stage4 21.475 2.121577e+09 6451.493 0.000 Inf 0.003 0.997 Purity 0.757 2.132000e+00 0.963 0.323 14.089 0.786 0.432 Rsquare = 0.158 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.66e-04 Wald test p = 5.96e-03 Score (logrank) test p = 3.99e-06 MVD in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.193 1.212000e+00 0.508 0.448 3.279 0.379 0.704 Age 0.025 1.026000e+00 0.033 0.962 1.094 0.775 0.438 RaceBlack -3.145 4.300000e-02 1.817 0.001 1.515 -1.731 0.083 · RaceWhite -1.661 1.900000e-01 1.457 0.011 3.300 -1.140 0.254 Stage2 18.381 9.612416e+07 15404.008 0.000 Inf 0.001 0.999 Stage3 19.949 4.609515e+08 15404.008 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.277 2.649300e+01 2.330 0.275 2550.643 1.406 0.160 Rsquare = 0.372 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.81e-04 Wald test p = 2.3e-01 Score (logrank) test p = 9.91e-15 MVD in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.130 1.139 0.195 0.777 1.669 0.668 0.504 Age 0.049 1.050 0.012 1.026 1.075 4.133 0.000 *** Gendermale -15.423 0.000 3464.268 0.000 Inf -0.004 0.996 RaceBlack -0.445 0.641 1.174 0.064 6.395 -0.379 0.705 RaceWhite 0.267 1.306 1.031 0.173 9.854 0.259 0.796 Stage2 0.315 1.370 0.375 0.657 2.856 0.839 0.402 Stage3 0.819 2.269 0.399 1.038 4.960 2.054 0.040 * Stage4 2.159 8.659 0.591 2.717 27.593 3.650 0.000 *** Purity 0.369 1.447 0.623 0.427 4.903 0.593 0.553 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 8.7e-05 Wald test p = 1.87e-05 Score (logrank) test p = 3.23e-07 MVD in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.112 0.894 0.224 0.576 1.386 -0.502 0.616 Age 0.050 1.052 0.021 1.010 1.095 2.427 0.015 * Gendermale 0.981 2.667 1.105 0.306 23.244 0.888 0.375 RaceBlack 16.601 16213386.533 6463.080 0.000 Inf 0.003 0.998 RaceWhite 15.900 8038369.753 6463.080 0.000 Inf 0.002 0.998 Stage2 0.687 1.988 1.073 0.243 16.285 0.641 0.522 Stage3 1.633 5.119 1.061 0.639 40.976 1.539 0.124 Stage4 2.221 9.216 1.202 0.875 97.126 1.848 0.065 · Purity 0.836 2.306 1.366 0.158 33.580 0.612 0.541 Rsquare = 0.107 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.88e-02 Wald test p = 6.78e-02 Score (logrank) test p = 2.31e-02 MVD in CESC (n=306): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.043 1.044 0.217 0.683 1.597 0.199 0.842 Age 0.011 1.011 0.010 0.992 1.031 1.139 0.255 RaceBlack 1.044 2.841 1.068 0.350 23.042 0.978 0.328 RaceWhite 0.831 2.297 1.016 0.313 16.825 0.818 0.413 Purity 0.563 1.757 0.736 0.415 7.436 0.765 0.444 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.74e-01 Wald test p = 7.09e-01 Score (logrank) test p = 7e-01 MVD in CHOL (n=36): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.072 1.075 0.494 0.408 2.831 0.145 0.884 Age 0.017 1.017 0.022 0.974 1.062 0.778 0.436 Gendermale 0.246 1.279 0.564 0.423 3.866 0.437 0.662 RaceBlack -0.317 0.728 1.507 0.038 13.959 -0.211 0.833 RaceWhite -1.062 0.346 0.893 0.060 1.989 -1.190 0.234 Stage2 0.678 1.970 0.684 0.515 7.529 0.991 0.322 Stage3 -15.545 0.000 6943.591 0.000 Inf -0.002 0.998 Stage4 0.831 2.295 0.672 0.615 8.562 1.237 0.216 Purity 2.053 7.795 1.566 0.362 167.711 1.311 0.190 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.78e-01 Wald test p = 6.51e-01 Score (logrank) test p = 4.86e-01 MVD in COAD (n=458): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.152 0.859 0.205 0.575 1.284 -0.741 0.459 Age 0.025 1.025 0.012 1.002 1.048 2.133 0.033 * Gendermale 0.177 1.193 0.273 0.698 2.038 0.646 0.518 RaceBlack -0.537 0.584 0.839 0.113 3.024 -0.641 0.522 RaceWhite -0.565 0.568 0.789 0.121 2.666 -0.717 0.474 Stage2 0.213 1.238 0.562 0.411 3.725 0.380 0.704 Stage3 0.854 2.349 0.552 0.796 6.936 1.546 0.122 Stage4 1.940 6.959 0.557 2.334 20.743 3.481 0.000 *** Purity -0.170 0.844 0.596 0.262 2.716 -0.284 0.776 Rsquare = 0.111 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.75e-04 Wald test p = 1.3e-04 Score (logrank) test p = 1.83e-05 MVD in DLBC (n=48): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.354 0.702 0.792 0.149 3.317 -0.446 0.655 Age -0.006 0.994 0.041 0.917 1.078 -0.147 0.883 Gendermale 0.519 1.680 1.084 0.201 14.059 0.479 0.632 RaceBlack 0.153 1.165 1.676 0.044 31.084 0.091 0.927 RaceWhite -2.218 0.109 1.334 0.008 1.487 -1.662 0.096 · Purity -2.180 0.113 2.168 0.002 7.919 -1.005 0.315 Rsquare = 0.135 (max possible = 5.58e-01 ) Likelihood ratio test p = 4.29e-01 Wald test p = 5.91e-01 Score (logrank) test p = 3.26e-01 MVD in ESCA (n=185): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.343 1.410 0.238 0.885 2.246 1.444 0.149 Age 0.009 1.009 0.014 0.981 1.037 0.631 0.528 Gendermale 0.518 1.679 0.540 0.582 4.842 0.959 0.338 RaceBlack 0.139 1.149 1.076 0.139 9.474 0.129 0.897 RaceWhite -0.026 0.975 0.454 0.400 2.371 -0.057 0.955 Stage2 0.869 2.385 0.669 0.643 8.844 1.300 0.193 Stage3 1.558 4.749 0.675 1.264 17.849 2.306 0.021 * Stage4 2.908 18.313 0.783 3.944 85.026 3.712 0.000 *** Purity 0.185 1.203 0.768 0.267 5.418 0.241 0.810 Rsquare = 0.154 (max possible = 9.32e-01 ) Likelihood ratio test p = 5.47e-03 Wald test p = 2.66e-03 Score (logrank) test p = 1.9e-04 MVD in GBM (n=153): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.098 1.103 0.154 0.815 1.492 0.634 0.526 Age 0.029 1.029 0.008 1.013 1.046 3.464 0.001 ** Gendermale -0.106 0.900 0.214 0.592 1.367 -0.496 0.620 RaceBlack 0.547 1.728 0.728 0.415 7.197 0.751 0.452 RaceWhite -0.248 0.780 0.615 0.234 2.602 -0.404 0.686 Purity -1.234 0.291 0.575 0.094 0.898 -2.148 0.032 * Rsquare = 0.132 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.07e-03 Wald test p = 4.98e-03 Score (logrank) test p = 4.23e-03 MVD in HNSC (n=522): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.064 1.066 0.108 0.862 1.317 0.589 0.556 Age 0.022 1.022 0.008 1.007 1.038 2.875 0.004 ** Gendermale -0.252 0.777 0.172 0.555 1.089 -1.466 0.143 RaceBlack 0.100 1.105 0.561 0.368 3.322 0.178 0.859 RaceWhite -0.261 0.770 0.511 0.283 2.097 -0.511 0.609 Stage2 0.599 1.820 0.545 0.626 5.290 1.099 0.272 Stage3 0.847 2.333 0.536 0.815 6.676 1.579 0.114 Stage4 1.248 3.483 0.510 1.282 9.465 2.447 0.014 * Purity -0.056 0.946 0.365 0.462 1.936 -0.152 0.879 Rsquare = 0.07 (max possible = 9.89e-01 ) Likelihood ratio test p = 4.6e-04 Wald test p = 1.25e-03 Score (logrank) test p = 9.13e-04 MVD in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.892 2.441 0.449 1.013 5.886 1.988 0.047 * Age 0.001 1.001 0.026 0.951 1.053 0.039 0.969 Gendermale -0.332 0.717 0.553 0.243 2.120 -0.601 0.548 RaceBlack 18.147 76042333.776 12546.327 0.000 Inf 0.001 0.999 RaceWhite 17.133 27596413.393 12546.327 0.000 Inf 0.001 0.999 Stage2 17.427 37006948.019 5464.155 0.000 Inf 0.003 0.997 Stage3 16.712 18109842.040 5464.155 0.000 Inf 0.003 0.998 Stage4 17.608 44375402.753 5464.155 0.000 Inf 0.003 0.997 Purity -2.061 0.127 1.089 0.015 1.076 -1.892 0.058 · Rsquare = 0.142 (max possible = 9.17e-01 ) Likelihood ratio test p = 3.56e-01 Wald test p = 6.52e-01 Score (logrank) test p = 5.07e-01 MVD in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.012 1.012 0.113 0.811 1.262 0.103 0.918 Age 0.027 1.027 0.008 1.010 1.044 3.191 0.001 ** Gendermale -0.286 0.751 0.183 0.525 1.075 -1.563 0.118 RaceBlack -0.023 0.977 0.567 0.322 2.967 -0.041 0.968 RaceWhite -0.398 0.672 0.512 0.246 1.834 -0.776 0.438 Stage2 0.362 1.437 0.555 0.484 4.265 0.652 0.514 Stage3 0.725 2.065 0.541 0.715 5.962 1.340 0.180 Stage4 1.145 3.143 0.512 1.151 8.578 2.235 0.025 * Purity 0.209 1.233 0.400 0.562 2.702 0.522 0.601 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.39e-04 Wald test p = 9.54e-04 Score (logrank) test p = 7.21e-04 MVD in KICH (n=66): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p MVD -0.422 6.560000e-01 0.507 0.243 1.769000e+00 -0.833 0.405 Age 0.077 1.080000e+00 0.029 1.020 1.144000e+00 2.623 0.009 Gendermale -0.844 4.300000e-01 0.726 0.104 1.784000e+00 -1.163 0.245 RaceBlack -17.848 0.000000e+00 6171.501 0.000 Inf -0.003 0.998 RaceWhite -2.550 7.800000e-02 1.156 0.008 7.530000e-01 -2.206 0.027 Stage2 16.455 1.401026e+07 0.845 2672473.722 7.344786e+07 19.466 0.000 Stage3 17.329 3.356280e+07 0.777 7319983.143 1.538885e+08 22.304 0.000 Stage4 19.601 3.255041e+08 0.891 56792450.263 1.865616e+09 22.003 0.000 Purity 1.437 4.208000e+00 3.658 0.003 5.471158e+03 0.393 0.694 signif MVD Age ** Gendermale RaceBlack RaceWhite * Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.349 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.37e-03 Wald test p = 2.52e-290 Score (logrank) test p = 1e-08 MVD in KIRC (n=533): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.076 1.079 0.189 0.745 1.564 0.404 0.686 Age 0.035 1.036 0.008 1.019 1.053 4.177 0.000 *** Gendermale -0.070 0.933 0.186 0.648 1.342 -0.376 0.707 RaceBlack 0.193 1.213 1.056 0.153 9.619 0.183 0.855 RaceWhite 0.169 1.184 1.015 0.162 8.648 0.166 0.868 Stage2 0.220 1.246 0.344 0.634 2.448 0.639 0.523 Stage3 0.819 2.268 0.230 1.443 3.562 3.553 0.000 *** Stage4 1.766 5.846 0.216 3.825 8.935 8.160 0.000 *** Purity -0.019 0.981 0.369 0.476 2.022 -0.053 0.958 Rsquare = 0.174 (max possible = 9.65e-01 ) Likelihood ratio test p = 9.61e-15 Wald test p = 1.17e-14 Score (logrank) test p = 5.74e-18 MVD in KIRP (n=290): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.388 0.678 0.389 0.316 1.455 -0.997 0.319 Age 0.004 1.004 0.016 0.974 1.035 0.268 0.789 Gendermale -0.442 0.643 0.388 0.301 1.375 -1.139 0.255 RaceBlack -1.950 0.142 1.191 0.014 1.467 -1.638 0.101 RaceWhite -2.026 0.132 1.173 0.013 1.313 -1.728 0.084 · Stage2 -0.362 0.696 1.057 0.088 5.524 -0.343 0.732 Stage3 1.593 4.919 0.430 2.117 11.432 3.703 0.000 *** Stage4 2.628 13.852 0.515 5.045 38.036 5.100 0.000 *** Purity -0.322 0.725 0.750 0.167 3.152 -0.430 0.667 Rsquare = 0.167 (max possible = 7.58e-01 ) Likelihood ratio test p = 7.69e-06 Wald test p = 2.27e-06 Score (logrank) test p = 4.07e-10 MVD in LAML (n=173): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.540 1.716 0.178 1.211 2.432 3.036 0.002 ** Age 0.034 1.035 0.008 1.019 1.051 4.341 0.000 *** Gendermale -0.099 0.906 0.212 0.598 1.372 -0.467 0.640 RaceBlack -0.612 0.542 1.109 0.062 4.767 -0.552 0.581 RaceWhite -0.997 0.369 1.024 0.050 2.744 -0.974 0.330 Rsquare = 0.207 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.9e-06 Wald test p = 3.05e-06 Score (logrank) test p = 1.63e-06 MVD in LGG (n=516): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.411 0.663 0.161 0.484 0.908 -2.558 0.011 * Age 0.060 1.062 0.008 1.046 1.078 7.854 0.000 *** Gendermale 0.055 1.056 0.195 0.720 1.548 0.279 0.780 RaceBlack 15.268 4271942.714 2131.360 0.000 Inf 0.007 0.994 RaceWhite 15.386 4807882.301 2131.360 0.000 Inf 0.007 0.994 Purity -0.874 0.417 0.405 0.189 0.924 -2.155 0.031 * Rsquare = 0.149 (max possible = 9.07e-01 ) Likelihood ratio test p = 4.37e-14 Wald test p = 1.75e-14 Score (logrank) test p = 1.36e-15 MVD in LIHC (n=371): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.015 1.015 0.100 0.835 1.233 0.148 0.882 Age 0.011 1.011 0.008 0.994 1.027 1.265 0.206 Gendermale -0.142 0.868 0.226 0.558 1.350 -0.628 0.530 RaceBlack 0.881 2.413 0.495 0.915 6.366 1.780 0.075 · RaceWhite -0.001 0.999 0.238 0.626 1.593 -0.006 0.995 Stage2 0.319 1.375 0.264 0.820 2.305 1.209 0.227 Stage3 0.946 2.575 0.236 1.622 4.089 4.009 0.000 *** Stage4 1.586 4.883 0.619 1.450 16.438 2.560 0.010 * Purity 0.570 1.769 0.461 0.717 4.362 1.238 0.216 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.2e-03 Wald test p = 7.09e-04 Score (logrank) test p = 2.69e-04 MVD in LUAD (n=515): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.113 1.119 0.123 0.879 1.425 0.915 0.360 Age 0.008 1.008 0.009 0.990 1.026 0.854 0.393 Gendermale 0.016 1.016 0.169 0.730 1.415 0.096 0.924 RaceBlack 16.114 9954741.551 1878.268 0.000 Inf 0.009 0.993 RaceWhite 16.318 12217253.015 1878.268 0.000 Inf 0.009 0.993 Stage2 0.845 2.327 0.202 1.566 3.459 4.178 0.000 *** Stage3 0.977 2.658 0.221 1.722 4.102 4.414 0.000 *** Stage4 1.013 2.754 0.333 1.433 5.294 3.038 0.002 ** Purity 0.571 1.771 0.343 0.904 3.468 1.666 0.096 · Rsquare = 0.098 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.68e-06 Wald test p = 2.25e-05 Score (logrank) test p = 2.66e-06 MVD in LUSC (n=501): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.111 1.117 0.094 0.929 1.343 1.178 0.239 Age 0.016 1.016 0.009 0.997 1.035 1.688 0.091 · Gendermale 0.404 1.498 0.195 1.023 2.195 2.076 0.038 * RaceBlack 0.007 1.007 0.607 0.306 3.310 0.011 0.991 RaceWhite -0.492 0.611 0.565 0.202 1.848 -0.872 0.383 Stage2 0.195 1.215 0.187 0.842 1.754 1.039 0.299 Stage3 0.602 1.825 0.214 1.199 2.778 2.806 0.005 ** Stage4 0.713 2.040 0.797 0.428 9.726 0.895 0.371 Purity -0.356 0.701 0.365 0.342 1.434 -0.974 0.330 Rsquare = 0.054 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.5e-02 Wald test p = 1.19e-02 Score (logrank) test p = 1.04e-02 MVD in MESO (n=87): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.430 1.538 0.211 1.017 2.324 2.041 0.041 * Age 0.015 1.015 0.016 0.984 1.047 0.917 0.359 Gendermale -0.177 0.838 0.332 0.437 1.605 -0.534 0.593 RaceBlack 0.676 1.966 1.549 0.094 40.928 0.437 0.662 RaceWhite -0.216 0.806 1.051 0.103 6.319 -0.206 0.837 Stage2 -0.316 0.729 0.464 0.293 1.812 -0.680 0.497 Stage3 -0.297 0.743 0.428 0.321 1.718 -0.694 0.488 Stage4 -0.313 0.731 0.478 0.286 1.867 -0.654 0.513 Purity -0.673 0.510 0.561 0.170 1.533 -1.198 0.231 Rsquare = 0.104 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.06e-01 Wald test p = 4.07e-01 Score (logrank) test p = 3.86e-01 MVD in OV (n=303): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.178 1.194 0.139 0.910 1.568 1.279 0.201 Age 0.039 1.039 0.008 1.022 1.057 4.609 0.000 *** RaceBlack 0.000 1.000 0.578 0.322 3.104 0.000 1.000 RaceWhite -0.117 0.890 0.517 0.323 2.452 -0.226 0.822 Purity -0.412 0.663 0.678 0.175 2.504 -0.607 0.544 Rsquare = 0.087 (max possible = 9.97e-01 ) Likelihood ratio test p = 5.76e-04 Wald test p = 5.15e-04 Score (logrank) test p = 4.26e-04 MVD in PAAD (n=179): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.100 0.905 0.140 0.688 1.191 -0.715 0.475 Age 0.023 1.023 0.011 1.002 1.046 2.109 0.035 * Gendermale -0.207 0.813 0.217 0.531 1.245 -0.952 0.341 RaceBlack -0.103 0.902 0.747 0.209 3.902 -0.138 0.891 RaceWhite 0.326 1.386 0.476 0.545 3.526 0.685 0.493 Stage2 0.638 1.892 0.438 0.802 4.467 1.455 0.146 Stage3 -0.308 0.735 1.097 0.086 6.304 -0.281 0.779 Stage4 0.317 1.372 0.831 0.269 6.997 0.381 0.703 Purity -0.647 0.524 0.408 0.235 1.165 -1.585 0.113 Rsquare = 0.091 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.14e-02 Wald test p = 1.01e-01 Score (logrank) test p = 9.53e-02 MVD in PCPG (n=181): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -1.102 0.332 0.807 0.068 1.617 -1.365 0.172 Age 0.033 1.033 0.029 0.976 1.094 1.125 0.261 Gendermale 1.583 4.869 0.913 0.813 29.151 1.733 0.083 · RaceBlack -0.057 0.945 21374.087 0.000 Inf 0.000 1.000 RaceWhite 17.347 34166050.024 16741.527 0.000 Inf 0.001 0.999 Purity 5.018 151.085 3.514 0.154 147966.652 1.428 0.153 Rsquare = 0.064 (max possible = 3.07e-01 ) Likelihood ratio test p = 9.08e-02 Wald test p = 3.04e-01 Score (logrank) test p = 2.48e-01 MVD in PRAD (n=498): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.688 1.990 0.475 0.785 5.045 1.449 0.147 Age 0.009 1.009 0.057 0.902 1.129 0.162 0.871 RaceBlack 14.968 3167446.366 6961.266 0.000 Inf 0.002 0.998 RaceWhite 16.084 9661495.002 6961.266 0.000 Inf 0.002 0.998 Purity 1.046 2.846 1.442 0.169 48.027 0.725 0.468 Rsquare = 0.011 (max possible = 1.83e-01 ) Likelihood ratio test p = 4.6e-01 Wald test p = 5.66e-01 Score (logrank) test p = 5.27e-01 MVD in READ (n=166): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.911 2.488 0.609 0.754 8.211 1.496 0.135 Age 0.137 1.146 0.054 1.031 1.274 2.532 0.011 * Gendermale -0.715 0.489 0.747 0.113 2.114 -0.958 0.338 RaceBlack 12.759 347542.053 10708.235 0.000 Inf 0.001 0.999 RaceWhite 11.427 91729.825 10708.235 0.000 Inf 0.001 0.999 Stage2 -2.144 0.117 1.299 0.009 1.495 -1.650 0.099 · Stage3 -0.518 0.596 0.935 0.095 3.723 -0.554 0.579 Stage4 -0.606 0.545 1.051 0.069 4.282 -0.577 0.564 Purity -0.392 0.676 1.409 0.043 10.686 -0.278 0.781 Rsquare = 0.233 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.71e-02 Wald test p = 2.32e-01 Score (logrank) test p = 3.45e-02 MVD in SARC (n=260): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.476 1.610 0.166 1.162 2.231 2.860 0.004 ** Age 0.022 1.022 0.008 1.006 1.039 2.688 0.007 ** Gendermale 0.003 1.003 0.221 0.650 1.548 0.015 0.988 RaceBlack -0.339 0.712 1.089 0.084 6.023 -0.312 0.755 RaceWhite -0.716 0.489 1.028 0.065 3.668 -0.696 0.486 Purity 1.087 2.965 0.567 0.976 9.012 1.916 0.055 · Rsquare = 0.074 (max possible = 9.75e-01 ) Likelihood ratio test p = 6.16e-03 Wald test p = 4.94e-03 Score (logrank) test p = 5.39e-03 MVD in SKCM (n=471): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.080 1.084 0.108 0.878 1.338 0.749 0.454 Age 0.018 1.018 0.005 1.008 1.029 3.491 0.000 *** Gendermale -0.055 0.946 0.157 0.695 1.288 -0.350 0.726 RaceWhite -1.275 0.279 0.402 0.127 0.614 -3.176 0.001 ** Stage2 0.296 1.344 0.220 0.874 2.068 1.346 0.178 Stage3 0.629 1.875 0.205 1.254 2.803 3.063 0.002 ** Stage4 1.365 3.917 0.352 1.963 7.816 3.874 0.000 *** Purity 1.030 2.802 0.339 1.440 5.450 3.035 0.002 ** Rsquare = 0.125 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.74e-08 Wald test p = 9.48e-09 Score (logrank) test p = 1.13e-09 MVD in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.331 1.392000e+00 0.265 0.829 2.339 1.251 0.211 Age 0.010 1.010000e+00 0.016 0.978 1.042 0.598 0.550 Gendermale 0.287 1.332000e+00 0.438 0.564 3.145 0.655 0.513 RaceWhite -1.142 3.190000e-01 0.633 0.092 1.105 -1.803 0.071 · Stage2 17.408 3.633729e+07 6299.991 0.000 Inf 0.003 0.998 Stage3 17.962 6.323862e+07 6299.991 0.000 Inf 0.003 0.998 Stage4 20.434 7.491242e+08 6299.991 0.000 Inf 0.003 0.997 Purity 0.311 1.365000e+00 0.942 0.216 8.645 0.331 0.741 Rsquare = 0.161 (max possible = 8.69e-01 ) Likelihood ratio test p = 3.56e-02 Wald test p = 2.49e-02 Score (logrank) test p = 1.73e-03 MVD in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD 0.035 1.035 0.117 0.823 1.302 0.296 0.767 Age 0.020 1.021 0.006 1.009 1.032 3.624 0.000 *** Gendermale -0.061 0.941 0.172 0.671 1.319 -0.352 0.725 RaceWhite -1.057 0.347 0.600 0.107 1.125 -1.763 0.078 · Stage2 0.162 1.176 0.233 0.745 1.856 0.697 0.486 Stage3 0.570 1.768 0.210 1.171 2.670 2.712 0.007 ** Stage4 1.138 3.121 0.400 1.425 6.837 2.845 0.004 ** Purity 1.145 3.144 0.369 1.524 6.486 3.100 0.002 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 1.06e-06 Wald test p = 1.65e-06 Score (logrank) test p = 6.44e-07 MVD in STAD (n=415): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.338 0.713 0.160 0.521 0.975 -2.116 0.034 * Age 0.029 1.030 0.010 1.009 1.051 2.841 0.005 ** Gendermale 0.210 1.233 0.212 0.814 1.869 0.989 0.323 RaceBlack 0.283 1.327 0.446 0.554 3.178 0.634 0.526 RaceWhite 0.119 1.127 0.245 0.697 1.821 0.487 0.626 Stage2 0.559 1.748 0.391 0.812 3.765 1.428 0.153 Stage3 1.001 2.721 0.365 1.330 5.566 2.741 0.006 ** Stage4 1.456 4.289 0.508 1.584 11.613 2.865 0.004 ** Purity -0.548 0.578 0.380 0.274 1.218 -1.441 0.150 Rsquare = 0.084 (max possible = 9.79e-01 ) Likelihood ratio test p = 2.68e-03 Wald test p = 4.79e-03 Score (logrank) test p = 3.43e-03 MVD in TGCT (n=150): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -4.926 7.000000e-03 35032.408 0 Inf 0.000 1.000 Age -1.826 1.610000e-01 1680.187 0 Inf -0.001 0.999 RaceBlack 4.618 1.013160e+02 17448519.782 0 Inf 0.000 1.000 RaceWhite -42.448 0.000000e+00 17844670.778 0 Inf 0.000 1.000 Stage2 -5.696 3.000000e-03 40502.649 0 Inf 0.000 1.000 Stage3 18.239 8.338689e+07 127935.352 0 Inf 0.000 1.000 Purity 30.366 1.541601e+13 195307.632 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.3e-03 MVD in THCA (n=509): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.055 0.947 0.553 0.320 2.800 -0.099 0.921 Age 0.148 1.159 0.030 1.094 1.228 4.994 0.000 *** Gendermale -0.040 0.961 0.649 0.269 3.428 -0.062 0.951 RaceBlack 16.949 22954424.694 6155.338 0.000 Inf 0.003 0.998 RaceWhite 16.747 18746877.174 6155.338 0.000 Inf 0.003 0.998 Stage2 -0.193 0.825 1.097 0.096 7.083 -0.176 0.861 Stage3 0.227 1.255 0.854 0.235 6.686 0.266 0.790 Stage4 1.673 5.330 0.975 0.788 36.046 1.716 0.086 · Purity 2.203 9.055 1.149 0.952 86.166 1.917 0.055 · Rsquare = 0.149 (max possible = 3.47e-01 ) Likelihood ratio test p = 2.53e-10 Wald test p = 4.07e-04 Score (logrank) test p = 5.32e-11 MVD in THYM (n=120): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -2.477 0.084 1.075 0.010 0.690 -2.305 0.021 * Age 0.021 1.021 0.032 0.959 1.086 0.651 0.515 Gendermale 0.134 1.144 0.754 0.261 5.011 0.178 0.859 RaceBlack -15.400 0.000 10561.777 0.000 Inf -0.001 0.999 RaceWhite 0.888 2.431 1.154 0.253 23.327 0.770 0.441 Purity 0.434 1.543 1.144 0.164 14.540 0.379 0.705 Rsquare = 0.094 (max possible = 4.51e-01 ) Likelihood ratio test p = 8.21e-02 Wald test p = 1.83e-01 Score (logrank) test p = 1.45e-01 MVD in UCEC (n=545): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.073 0.930 0.224 0.600 1.441 -0.325 0.745 Age 0.049 1.050 0.016 1.018 1.084 3.077 0.002 ** RaceBlack -0.439 0.645 0.797 0.135 3.074 -0.551 0.582 RaceWhite -0.541 0.582 0.747 0.135 2.516 -0.724 0.469 Purity 0.434 1.543 0.648 0.433 5.493 0.669 0.503 Rsquare = 0.039 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.83e-02 Wald test p = 5.51e-02 Score (logrank) test p = 5.21e-02 MVD in UCS (n=57): Model: Surv(OS, EVENT) ~ `MVD` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.586 0.557 0.345 0.283 1.094 -1.699 0.089 · Age 0.056 1.058 0.026 1.006 1.112 2.188 0.029 * RaceBlack 17.768 52041403.326 6627.668 0.000 Inf 0.003 0.998 RaceWhite 18.021 67020586.399 6627.668 0.000 Inf 0.003 0.998 Purity -0.277 0.758 1.091 0.089 6.436 -0.254 0.800 Rsquare = 0.171 (max possible = 9.83e-01 ) Likelihood ratio test p = 8.28e-02 Wald test p = 1.52e-01 Score (logrank) test p = 9.73e-02 MVD in UVM (n=80): Model: Surv(OS, EVENT) ~ `MVD` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVD -0.658 0.518 0.473 0.205 1.309 -1.391 0.164 Age 0.037 1.038 0.019 1.000 1.078 1.963 0.050 · Gendermale 0.302 1.352 0.473 0.535 3.418 0.638 0.523 Stage3 0.409 1.506 0.503 0.561 4.038 0.813 0.416 Stage4 4.031 56.306 1.236 4.996 634.608 3.262 0.001 ** Purity 1.560 4.759 1.279 0.388 58.369 1.220 0.223 Rsquare = 0.271 (max possible = 8.72e-01 ) Likelihood ratio test p = 4.57e-04 Wald test p = 1.79e-03 Score (logrank) test p = 1.07e-09 MVK in ACC (n=79): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.267 1.30600e+00 0.192 0.896 1.904 1.390 0.165 Age 0.011 1.01100e+00 0.015 0.982 1.041 0.732 0.464 Gendermale 0.463 1.58900e+00 0.426 0.689 3.662 1.087 0.277 RaceBlack -0.799 4.50000e-01 12187.648 0.000 Inf 0.000 1.000 RaceWhite 16.163 1.04547e+07 10396.949 0.000 Inf 0.002 0.999 Purity 2.397 1.09880e+01 2.421 0.096 1262.690 0.990 0.322 Rsquare = 0.096 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.72e-01 Wald test p = 6.9e-01 Score (logrank) test p = 5.24e-01 MVK in BLCA (n=408): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.065 1.067 0.117 0.849 1.341 0.554 0.579 Age 0.033 1.033 0.009 1.016 1.051 3.817 0.000 *** Gendermale -0.176 0.839 0.179 0.591 1.191 -0.984 0.325 RaceBlack 0.737 2.089 0.449 0.867 5.036 1.642 0.101 RaceWhite 0.152 1.164 0.360 0.575 2.360 0.422 0.673 Stage2 14.463 1911413.942 1870.337 0.000 Inf 0.008 0.994 Stage3 14.911 2989587.819 1870.337 0.000 Inf 0.008 0.994 Stage4 15.441 5081846.379 1870.337 0.000 Inf 0.008 0.993 Purity 0.096 1.100 0.349 0.555 2.180 0.274 0.784 Rsquare = 0.131 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.77e-07 Wald test p = 1.07e-06 Score (logrank) test p = 2.96e-07 MVK in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.102 0.903 0.171 0.646 1.261 -0.600 0.548 Age 0.036 1.036 0.008 1.021 1.052 4.730 0.000 *** Gendermale 0.068 1.071 1.008 0.148 7.725 0.068 0.946 RaceBlack 0.001 1.001 0.619 0.298 3.368 0.002 0.999 RaceWhite -0.243 0.784 0.597 0.243 2.527 -0.407 0.684 Stage2 0.419 1.520 0.304 0.838 2.760 1.378 0.168 Stage3 1.216 3.374 0.316 1.815 6.269 3.846 0.000 *** Stage4 2.573 13.107 0.401 5.974 28.759 6.418 0.000 *** Purity 0.550 1.733 0.423 0.756 3.975 1.299 0.194 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.11e-12 Wald test p = 6.93e-16 Score (logrank) test p = 8.03e-22 MVK in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.027 1.028000e+00 0.358 0.509 2.075 0.076 0.940 Age 0.011 1.011000e+00 0.018 0.976 1.046 0.595 0.552 RaceBlack -0.921 3.980000e-01 1.109 0.045 3.501 -0.831 0.406 RaceWhite -1.237 2.900000e-01 1.116 0.033 2.586 -1.109 0.268 Stage2 18.685 1.302887e+08 6479.587 0.000 Inf 0.003 0.998 Stage3 20.103 5.379857e+08 6479.587 0.000 Inf 0.003 0.998 Stage4 21.411 1.990119e+09 6479.587 0.000 Inf 0.003 0.997 Purity 0.755 2.128000e+00 0.958 0.325 13.925 0.788 0.431 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.2e-04 Wald test p = 7.14e-03 Score (logrank) test p = 4.71e-06 MVK in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.396 1.485000e+00 0.673 0.397 5.559 0.587 0.557 Age 0.030 1.030000e+00 0.028 0.975 1.088 1.067 0.286 RaceBlack -3.044 4.800000e-02 1.782 0.001 1.566 -1.708 0.088 · RaceWhite -1.626 1.970000e-01 1.448 0.012 3.363 -1.122 0.262 Stage2 18.275 8.643884e+07 14706.385 0.000 Inf 0.001 0.999 Stage3 20.077 5.239154e+08 14706.385 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.515 3.362100e+01 2.529 0.236 4782.498 1.390 0.165 Rsquare = 0.374 (max possible = 6.68e-01 ) Likelihood ratio test p = 2.61e-04 Wald test p = 2.21e-01 Score (logrank) test p = 1.07e-14 MVK in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.059 0.942 0.288 0.536 1.656 -0.207 0.836 Age 0.048 1.050 0.012 1.025 1.074 4.052 0.000 *** Gendermale -15.348 0.000 3459.798 0.000 Inf -0.004 0.996 RaceBlack -0.442 0.642 1.175 0.064 6.424 -0.377 0.706 RaceWhite 0.233 1.263 1.034 0.167 9.577 0.226 0.821 Stage2 0.328 1.388 0.374 0.666 2.891 0.876 0.381 Stage3 0.873 2.394 0.397 1.100 5.211 2.199 0.028 * Stage4 2.186 8.903 0.616 2.662 29.776 3.550 0.000 *** Purity 0.337 1.401 0.631 0.407 4.824 0.535 0.593 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.02e-04 Wald test p = 2e-05 Score (logrank) test p = 4.07e-07 MVK in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.307 0.736 0.353 0.368 1.470 -0.869 0.385 Age 0.048 1.049 0.021 1.007 1.093 2.315 0.021 * Gendermale 1.038 2.824 1.107 0.323 24.715 0.938 0.348 RaceBlack 16.539 15233600.614 6320.038 0.000 Inf 0.003 0.998 RaceWhite 15.871 7812876.191 6320.038 0.000 Inf 0.003 0.998 Stage2 0.823 2.278 1.082 0.273 18.986 0.761 0.447 Stage3 1.826 6.211 1.090 0.733 52.642 1.675 0.094 · Stage4 2.366 10.652 1.213 0.989 114.713 1.951 0.051 · Purity 0.966 2.627 1.273 0.217 31.876 0.759 0.448 Rsquare = 0.109 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.28e-02 Wald test p = 6.4e-02 Score (logrank) test p = 1.97e-02 MVK in CESC (n=306): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.386 0.680 0.241 0.424 1.090 -1.602 0.109 Age 0.013 1.013 0.010 0.993 1.033 1.300 0.194 RaceBlack 1.159 3.185 1.070 0.391 25.945 1.083 0.279 RaceWhite 0.806 2.239 1.015 0.306 16.367 0.794 0.427 Purity 0.616 1.851 0.726 0.446 7.687 0.848 0.396 Rsquare = 0.025 (max possible = 8.91e-01 ) Likelihood ratio test p = 3.38e-01 Wald test p = 3.62e-01 Score (logrank) test p = 3.57e-01 MVK in CHOL (n=36): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.072 0.930 0.344 0.474 1.827 -0.209 0.834 Age 0.016 1.016 0.023 0.972 1.062 0.712 0.477 Gendermale 0.254 1.289 0.563 0.428 3.888 0.451 0.652 RaceBlack -0.371 0.690 1.484 0.038 12.638 -0.250 0.803 RaceWhite -1.066 0.344 0.887 0.060 1.960 -1.202 0.230 Stage2 0.656 1.928 0.669 0.520 7.147 0.982 0.326 Stage3 -15.667 0.000 6997.855 0.000 Inf -0.002 0.998 Stage4 0.738 2.092 0.777 0.456 9.591 0.950 0.342 Purity 1.982 7.257 1.566 0.337 156.289 1.265 0.206 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.76e-01 Wald test p = 6.35e-01 Score (logrank) test p = 4.66e-01 MVK in COAD (n=458): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.219 1.245 0.262 0.745 2.080 0.835 0.404 Age 0.023 1.024 0.011 1.001 1.047 2.031 0.042 * Gendermale 0.257 1.293 0.274 0.756 2.212 0.939 0.348 RaceBlack -0.433 0.648 0.827 0.128 3.280 -0.524 0.600 RaceWhite -0.440 0.644 0.772 0.142 2.927 -0.570 0.569 Stage2 0.185 1.204 0.563 0.399 3.630 0.329 0.742 Stage3 0.811 2.249 0.550 0.765 6.614 1.473 0.141 Stage4 1.880 6.553 0.554 2.214 19.394 3.396 0.001 ** Purity -0.216 0.806 0.601 0.248 2.615 -0.360 0.719 Rsquare = 0.112 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.53e-04 Wald test p = 1.2e-04 Score (logrank) test p = 1.73e-05 MVK in DLBC (n=48): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.377 1.457 1.213 0.135 15.705 0.311 0.756 Age -0.004 0.996 0.042 0.918 1.081 -0.099 0.921 Gendermale 0.776 2.172 1.130 0.237 19.880 0.687 0.492 RaceBlack 0.467 1.596 1.604 0.069 37.024 0.291 0.771 RaceWhite -2.160 0.115 1.302 0.009 1.479 -1.659 0.097 · Purity -2.002 0.135 2.072 0.002 7.837 -0.966 0.334 Rsquare = 0.133 (max possible = 5.58e-01 ) Likelihood ratio test p = 4.42e-01 Wald test p = 5.93e-01 Score (logrank) test p = 3.36e-01 MVK in ESCA (n=185): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.234 1.264 0.243 0.785 2.033 0.964 0.335 Age 0.012 1.012 0.014 0.984 1.041 0.842 0.400 Gendermale 0.506 1.658 0.539 0.576 4.770 0.938 0.348 RaceBlack 0.289 1.336 1.069 0.164 10.854 0.271 0.787 RaceWhite 0.005 1.005 0.453 0.414 2.441 0.011 0.991 Stage2 0.735 2.085 0.658 0.575 7.567 1.118 0.264 Stage3 1.508 4.517 0.675 1.204 16.944 2.235 0.025 * Stage4 2.978 19.648 0.788 4.195 92.031 3.780 0.000 *** Purity 0.069 1.071 0.777 0.234 4.908 0.088 0.930 Rsquare = 0.147 (max possible = 9.32e-01 ) Likelihood ratio test p = 8.28e-03 Wald test p = 4.12e-03 Score (logrank) test p = 3.13e-04 MVK in GBM (n=153): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.372 1.451 0.227 0.931 2.263 1.643 0.100 Age 0.029 1.029 0.008 1.013 1.046 3.509 0.000 *** Gendermale -0.136 0.873 0.215 0.573 1.329 -0.633 0.527 RaceBlack 0.334 1.397 0.739 0.328 5.946 0.452 0.651 RaceWhite -0.425 0.654 0.625 0.192 2.224 -0.680 0.496 Purity -1.283 0.277 0.538 0.097 0.796 -2.383 0.017 * Rsquare = 0.146 (max possible = 9.98e-01 ) Likelihood ratio test p = 1.57e-03 Wald test p = 1.9e-03 Score (logrank) test p = 1.66e-03 MVK in HNSC (n=522): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.072 0.930 0.129 0.722 1.198 -0.560 0.576 Age 0.022 1.023 0.008 1.007 1.038 2.923 0.003 ** Gendermale -0.247 0.781 0.172 0.558 1.094 -1.439 0.150 RaceBlack 0.169 1.184 0.562 0.394 3.563 0.301 0.764 RaceWhite -0.229 0.795 0.512 0.292 2.168 -0.448 0.654 Stage2 0.622 1.863 0.544 0.642 5.407 1.144 0.253 Stage3 0.854 2.349 0.537 0.820 6.723 1.591 0.112 Stage4 1.253 3.502 0.510 1.289 9.513 2.458 0.014 * Purity -0.032 0.969 0.364 0.475 1.976 -0.087 0.931 Rsquare = 0.07 (max possible = 9.89e-01 ) Likelihood ratio test p = 4.66e-04 Wald test p = 1.23e-03 Score (logrank) test p = 8.92e-04 MVK in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.274 7.600000e-01 0.455 0.311 1.857 -0.601 0.548 Age 0.009 1.009000e+00 0.025 0.960 1.060 0.349 0.727 Gendermale -0.136 8.730000e-01 0.541 0.302 2.518 -0.252 0.801 RaceBlack 19.063 1.901796e+08 12128.947 0.000 Inf 0.002 0.999 RaceWhite 18.179 7.851554e+07 12128.947 0.000 Inf 0.001 0.999 Stage2 17.379 3.527712e+07 5268.026 0.000 Inf 0.003 0.997 Stage3 16.503 1.469253e+07 5268.026 0.000 Inf 0.003 0.998 Stage4 17.425 3.694153e+07 5268.026 0.000 Inf 0.003 0.997 Purity -1.385 2.500000e-01 1.105 0.029 2.182 -1.254 0.210 Rsquare = 0.092 (max possible = 9.17e-01 ) Likelihood ratio test p = 7.12e-01 Wald test p = 9.33e-01 Score (logrank) test p = 8.36e-01 MVK in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.043 0.958 0.139 0.730 1.258 -0.308 0.758 Age 0.027 1.027 0.008 1.011 1.045 3.202 0.001 ** Gendermale -0.283 0.753 0.183 0.527 1.078 -1.551 0.121 RaceBlack 0.003 1.003 0.568 0.330 3.050 0.005 0.996 RaceWhite -0.387 0.679 0.513 0.248 1.857 -0.754 0.451 Stage2 0.372 1.450 0.554 0.490 4.296 0.671 0.502 Stage3 0.731 2.076 0.541 0.719 5.995 1.350 0.177 Stage4 1.147 3.149 0.512 1.154 8.589 2.240 0.025 * Purity 0.211 1.234 0.399 0.565 2.699 0.528 0.598 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.28e-04 Wald test p = 9.16e-04 Score (logrank) test p = 6.85e-04 MVK in KICH (n=66): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p MVK -0.022 9.790000e-01 0.808 0.201 4.766000e+00 -0.027 0.979 Age 0.076 1.079000e+00 0.029 1.019 1.142000e+00 2.623 0.009 Gendermale -0.902 4.060000e-01 0.728 0.097 1.689000e+00 -1.240 0.215 RaceBlack -17.135 0.000000e+00 6210.774 0.000 Inf -0.003 0.998 RaceWhite -1.963 1.400000e-01 1.160 0.014 1.363000e+00 -1.693 0.090 Stage2 16.088 9.706436e+06 0.848 1840940.502 5.117759e+07 18.967 0.000 Stage3 17.177 2.884143e+07 0.777 6284131.414 1.323696e+08 22.094 0.000 Stage4 19.636 3.372777e+08 0.900 57755863.745 1.969605e+09 21.809 0.000 Purity 1.072 2.921000e+00 3.595 0.003 3.354832e+03 0.298 0.766 signif MVK Age ** Gendermale RaceBlack RaceWhite · Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.346 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.54e-03 Wald test p = 8.88e-282 Score (logrank) test p = 1.05e-08 MVK in KIRC (n=533): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.190 0.827 0.106 0.672 1.017 -1.802 0.072 · Age 0.035 1.036 0.008 1.019 1.053 4.134 0.000 *** Gendermale -0.125 0.883 0.185 0.614 1.268 -0.676 0.499 RaceBlack 0.427 1.533 1.064 0.191 12.336 0.402 0.688 RaceWhite 0.265 1.304 1.017 0.178 9.573 0.261 0.794 Stage2 0.217 1.243 0.344 0.633 2.440 0.631 0.528 Stage3 0.866 2.378 0.231 1.511 3.743 3.745 0.000 *** Stage4 1.798 6.035 0.217 3.945 9.232 8.286 0.000 *** Purity -0.025 0.975 0.367 0.475 2.003 -0.069 0.945 Rsquare = 0.18 (max possible = 9.65e-01 ) Likelihood ratio test p = 2.27e-15 Wald test p = 2.71e-15 Score (logrank) test p = 1.31e-18 MVK in KIRP (n=290): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -1.201 0.301 0.308 0.164 0.550 -3.896 0.000 *** Age 0.015 1.015 0.016 0.984 1.048 0.968 0.333 Gendermale -0.463 0.630 0.383 0.297 1.334 -1.208 0.227 RaceBlack -2.842 0.058 1.187 0.006 0.597 -2.394 0.017 * RaceWhite -2.954 0.052 1.164 0.005 0.510 -2.538 0.011 * Stage2 -0.202 0.817 1.068 0.101 6.622 -0.189 0.850 Stage3 1.719 5.578 0.435 2.376 13.094 3.948 0.000 *** Stage4 3.331 27.973 0.565 9.241 84.679 5.895 0.000 *** Purity -0.209 0.811 0.734 0.192 3.419 -0.285 0.775 Rsquare = 0.218 (max possible = 7.58e-01 ) Likelihood ratio test p = 1.9e-08 Wald test p = 5.04e-07 Score (logrank) test p = 6.52e-12 MVK in LAML (n=173): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.679 1.973 0.222 1.277 3.047 3.062 0.002 ** Age 0.040 1.041 0.008 1.024 1.057 4.998 0.000 *** Gendermale -0.198 0.820 0.212 0.542 1.242 -0.936 0.349 RaceBlack -0.838 0.433 1.118 0.048 3.872 -0.749 0.454 RaceWhite -1.119 0.327 1.029 0.043 2.455 -1.087 0.277 Rsquare = 0.211 (max possible = 9.96e-01 ) Likelihood ratio test p = 1.29e-06 Wald test p = 4.36e-06 Score (logrank) test p = 2.49e-06 MVK in LGG (n=516): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.466 0.628 0.175 0.445 0.885 -2.657 0.008 ** Age 0.066 1.068 0.008 1.052 1.084 8.377 0.000 *** Gendermale 0.026 1.026 0.196 0.698 1.508 0.130 0.897 RaceBlack 15.168 3867545.686 2127.783 0.000 Inf 0.007 0.994 RaceWhite 15.336 4576091.735 2127.783 0.000 Inf 0.007 0.994 Purity -0.987 0.373 0.407 0.168 0.827 -2.425 0.015 * Rsquare = 0.15 (max possible = 9.07e-01 ) Likelihood ratio test p = 3.74e-14 Wald test p = 4.62e-14 Score (logrank) test p = 4.4e-15 MVK in LIHC (n=371): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.062 0.940 0.129 0.731 1.209 -0.481 0.630 Age 0.011 1.011 0.008 0.995 1.028 1.380 0.168 Gendermale -0.144 0.866 0.225 0.558 1.345 -0.640 0.522 RaceBlack 0.896 2.449 0.489 0.938 6.390 1.830 0.067 · RaceWhite 0.000 1.000 0.237 0.629 1.590 -0.001 0.999 Stage2 0.293 1.340 0.265 0.798 2.251 1.106 0.269 Stage3 0.959 2.608 0.236 1.643 4.140 4.067 0.000 *** Stage4 1.586 4.884 0.619 1.452 16.424 2.563 0.010 * Purity 0.576 1.779 0.458 0.725 4.368 1.258 0.209 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.1e-03 Wald test p = 6.78e-04 Score (logrank) test p = 2.54e-04 MVK in LUAD (n=515): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.160 0.852 0.150 0.635 1.143 -1.067 0.286 Age 0.006 1.006 0.009 0.988 1.024 0.690 0.490 Gendermale 0.037 1.038 0.170 0.743 1.449 0.219 0.827 RaceBlack 15.990 8795303.008 1886.992 0.000 Inf 0.008 0.993 RaceWhite 16.160 10429002.713 1886.992 0.000 Inf 0.009 0.993 Stage2 0.881 2.413 0.201 1.626 3.580 4.375 0.000 *** Stage3 1.049 2.854 0.220 1.854 4.395 4.762 0.000 *** Stage4 0.989 2.689 0.334 1.396 5.179 2.959 0.003 ** Purity 0.659 1.933 0.348 0.978 3.821 1.896 0.058 · Rsquare = 0.099 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.48e-06 Wald test p = 1.92e-05 Score (logrank) test p = 2.16e-06 MVK in LUSC (n=501): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.041 0.959 0.129 0.745 1.235 -0.322 0.747 Age 0.017 1.017 0.009 0.998 1.036 1.779 0.075 · Gendermale 0.437 1.547 0.193 1.059 2.261 2.257 0.024 * RaceBlack 0.012 1.012 0.605 0.309 3.314 0.020 0.984 RaceWhite -0.509 0.601 0.563 0.199 1.810 -0.905 0.365 Stage2 0.212 1.236 0.187 0.857 1.782 1.135 0.256 Stage3 0.613 1.846 0.216 1.208 2.821 2.834 0.005 ** Stage4 0.768 2.156 0.793 0.455 10.201 0.968 0.333 Purity -0.337 0.714 0.366 0.349 1.462 -0.921 0.357 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.34e-02 Wald test p = 1.8e-02 Score (logrank) test p = 1.55e-02 MVK in MESO (n=87): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.877 2.404 0.249 1.475 3.920 3.517 0.000 *** Age 0.019 1.019 0.016 0.987 1.053 1.167 0.243 Gendermale -0.209 0.811 0.331 0.424 1.553 -0.631 0.528 RaceBlack 0.378 1.460 1.534 0.072 29.528 0.247 0.805 RaceWhite -0.431 0.650 1.046 0.084 5.044 -0.412 0.680 Stage2 -0.044 0.957 0.465 0.385 2.380 -0.094 0.925 Stage3 -0.025 0.975 0.417 0.431 2.208 -0.061 0.952 Stage4 0.030 1.031 0.477 0.405 2.626 0.064 0.949 Purity -0.871 0.419 0.571 0.137 1.282 -1.525 0.127 Rsquare = 0.184 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.42e-02 Wald test p = 3.22e-02 Score (logrank) test p = 2.51e-02 MVK in OV (n=303): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.095 1.099 0.137 0.841 1.437 0.693 0.488 Age 0.036 1.037 0.008 1.020 1.053 4.419 0.000 *** RaceBlack -0.004 0.996 0.581 0.319 3.111 -0.007 0.994 RaceWhite -0.104 0.901 0.521 0.325 2.504 -0.199 0.842 Purity -0.519 0.595 0.668 0.161 2.203 -0.777 0.437 Rsquare = 0.083 (max possible = 9.97e-01 ) Likelihood ratio test p = 9.41e-04 Wald test p = 8.4e-04 Score (logrank) test p = 6.81e-04 MVK in PAAD (n=179): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.133 0.875 0.212 0.578 1.326 -0.629 0.529 Age 0.022 1.023 0.011 1.001 1.045 2.046 0.041 * Gendermale -0.199 0.819 0.218 0.535 1.255 -0.916 0.360 RaceBlack -0.010 0.990 0.739 0.233 4.211 -0.013 0.989 RaceWhite 0.362 1.436 0.474 0.567 3.634 0.764 0.445 Stage2 0.541 1.718 0.456 0.703 4.197 1.187 0.235 Stage3 -0.379 0.684 1.115 0.077 6.087 -0.340 0.734 Stage4 0.180 1.197 0.828 0.236 6.070 0.217 0.828 Purity -0.667 0.513 0.408 0.231 1.141 -1.636 0.102 Rsquare = 0.091 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.39e-02 Wald test p = 1.09e-01 Score (logrank) test p = 1.04e-01 MVK in PCPG (n=181): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.942 0.390 0.767 0.087 1.751 -1.229 0.219 Age 0.045 1.046 0.030 0.987 1.109 1.521 0.128 Gendermale 1.672 5.325 0.940 0.844 33.592 1.779 0.075 · RaceBlack -0.409 0.664 21563.289 0.000 Inf 0.000 1.000 RaceWhite 16.771 19206639.722 17479.130 0.000 Inf 0.001 0.999 Purity 6.151 469.221 3.410 0.588 374730.643 1.804 0.071 · Rsquare = 0.064 (max possible = 3.07e-01 ) Likelihood ratio test p = 9.47e-02 Wald test p = 3.07e-01 Score (logrank) test p = 2.42e-01 MVK in PRAD (n=498): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.669 1.953 0.535 0.684 5.579 1.250 0.211 Age 0.008 1.008 0.058 0.901 1.129 0.144 0.885 RaceBlack 15.215 4052500.458 6948.149 0.000 Inf 0.002 0.998 RaceWhite 16.308 12087814.628 6948.149 0.000 Inf 0.002 0.998 Purity 1.211 3.356 1.390 0.220 51.215 0.871 0.384 Rsquare = 0.01 (max possible = 1.83e-01 ) Likelihood ratio test p = 5.3e-01 Wald test p = 6.33e-01 Score (logrank) test p = 5.86e-01 MVK in READ (n=166): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.263 0.769 0.724 0.186 3.179 -0.363 0.716 Age 0.108 1.114 0.044 1.022 1.215 2.450 0.014 * Gendermale -0.362 0.696 0.698 0.177 2.735 -0.519 0.604 RaceBlack 13.249 567622.821 10135.432 0.000 Inf 0.001 0.999 RaceWhite 12.234 205588.598 10135.432 0.000 Inf 0.001 0.999 Stage2 -1.907 0.149 1.265 0.012 1.774 -1.507 0.132 Stage3 -0.565 0.569 0.936 0.091 3.559 -0.603 0.546 Stage4 -0.150 0.861 0.954 0.133 5.585 -0.157 0.875 Purity 0.286 1.331 1.399 0.086 20.680 0.205 0.838 Rsquare = 0.211 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.55e-02 Wald test p = 2.4e-01 Score (logrank) test p = 4.87e-02 MVK in SARC (n=260): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.308 1.361 0.144 1.027 1.804 2.144 0.032 * Age 0.023 1.023 0.008 1.007 1.039 2.826 0.005 ** Gendermale -0.013 0.987 0.223 0.637 1.529 -0.059 0.953 RaceBlack -0.031 0.970 1.087 0.115 8.167 -0.028 0.978 RaceWhite -0.388 0.679 1.022 0.091 5.034 -0.379 0.705 Purity 0.972 2.642 0.585 0.840 8.311 1.661 0.097 · Rsquare = 0.059 (max possible = 9.75e-01 ) Likelihood ratio test p = 2.69e-02 Wald test p = 2.58e-02 Score (logrank) test p = 2.58e-02 MVK in SKCM (n=471): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.228 1.256 0.113 1.006 1.568 2.013 0.044 * Age 0.018 1.018 0.005 1.008 1.028 3.417 0.001 ** Gendermale -0.064 0.938 0.157 0.689 1.277 -0.409 0.683 RaceWhite -1.248 0.287 0.402 0.131 0.632 -3.103 0.002 ** Stage2 0.263 1.301 0.218 0.849 1.993 1.210 0.226 Stage3 0.669 1.953 0.206 1.305 2.923 3.252 0.001 ** Stage4 1.367 3.925 0.352 1.970 7.819 3.888 0.000 *** Purity 1.021 2.775 0.340 1.425 5.404 3.001 0.003 ** Rsquare = 0.132 (max possible = 9.92e-01 ) Likelihood ratio test p = 3.76e-09 Wald test p = 2.58e-09 Score (logrank) test p = 2.74e-10 MVK in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.392 1.479000e+00 0.356 0.737 2.971 1.101 0.271 Age 0.010 1.010000e+00 0.016 0.978 1.043 0.589 0.556 Gendermale 0.319 1.375000e+00 0.444 0.576 3.282 0.718 0.473 RaceWhite -1.184 3.060000e-01 0.631 0.089 1.053 -1.877 0.060 · Stage2 17.412 3.648473e+07 6244.788 0.000 Inf 0.003 0.998 Stage3 17.970 6.369592e+07 6244.788 0.000 Inf 0.003 0.998 Stage4 20.439 7.526694e+08 6244.788 0.000 Inf 0.003 0.997 Purity 0.208 1.231000e+00 0.947 0.192 7.879 0.220 0.826 Rsquare = 0.157 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.09e-02 Wald test p = 3.23e-02 Score (logrank) test p = 2.4e-03 MVK in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.184 1.202 0.122 0.946 1.526 1.508 0.132 Age 0.020 1.020 0.006 1.009 1.031 3.553 0.000 *** Gendermale -0.074 0.929 0.173 0.662 1.302 -0.428 0.669 RaceWhite -1.057 0.347 0.600 0.107 1.126 -1.762 0.078 · Stage2 0.148 1.160 0.230 0.740 1.819 0.646 0.518 Stage3 0.613 1.846 0.211 1.220 2.793 2.902 0.004 ** Stage4 1.138 3.120 0.399 1.426 6.826 2.848 0.004 ** Purity 1.134 3.109 0.370 1.505 6.420 3.065 0.002 ** Rsquare = 0.14 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.15e-07 Wald test p = 7.04e-07 Score (logrank) test p = 2.64e-07 MVK in STAD (n=415): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.250 0.779 0.172 0.556 1.092 -1.451 0.147 Age 0.028 1.028 0.010 1.008 1.049 2.706 0.007 ** Gendermale 0.191 1.211 0.212 0.799 1.835 0.901 0.368 RaceBlack 0.251 1.286 0.446 0.536 3.085 0.563 0.573 RaceWhite 0.167 1.182 0.250 0.725 1.928 0.671 0.502 Stage2 0.517 1.677 0.390 0.781 3.603 1.325 0.185 Stage3 0.915 2.498 0.363 1.226 5.088 2.521 0.012 * Stage4 1.316 3.729 0.504 1.389 10.010 2.612 0.009 ** Purity -0.502 0.605 0.378 0.289 1.270 -1.328 0.184 Rsquare = 0.076 (max possible = 9.79e-01 ) Likelihood ratio test p = 6.41e-03 Wald test p = 8.95e-03 Score (logrank) test p = 6.88e-03 MVK in TGCT (n=150): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -2.912 5.400000e-02 23896.03 0 Inf 0.000 1.000 Age -1.720 1.790000e-01 1619.78 0 Inf -0.001 0.999 RaceBlack 8.717 6.105312e+03 22655440.72 0 Inf 0.000 1.000 RaceWhite -36.911 0.000000e+00 22743547.58 0 Inf 0.000 1.000 Stage2 -4.492 1.100000e-02 38998.58 0 Inf 0.000 1.000 Stage3 18.539 1.125939e+08 182694.85 0 Inf 0.000 1.000 Purity 19.503 2.950178e+08 211422.67 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.05e-03 MVK in THCA (n=509): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.832 2.297 0.804 0.475 11.103 1.035 0.301 Age 0.152 1.164 0.029 1.099 1.232 5.216 0.000 *** Gendermale -0.227 0.797 0.658 0.220 2.893 -0.345 0.730 RaceBlack 17.749 51094730.379 9433.788 0.000 Inf 0.002 0.998 RaceWhite 17.618 44806878.828 9433.788 0.000 Inf 0.002 0.999 Stage2 -0.122 0.885 1.081 0.106 7.369 -0.112 0.910 Stage3 0.451 1.570 0.884 0.277 8.880 0.510 0.610 Stage4 2.077 7.983 1.084 0.954 66.825 1.916 0.055 · Purity 2.049 7.762 1.071 0.951 63.353 1.913 0.056 · Rsquare = 0.151 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.55e-10 Wald test p = 4.33e-04 Score (logrank) test p = 1.16e-10 MVK in THYM (n=120): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.635 1.886 1.090 0.223 15.988 0.582 0.561 Age 0.049 1.050 0.031 0.988 1.116 1.564 0.118 Gendermale -0.196 0.822 0.727 0.198 3.417 -0.270 0.787 RaceBlack -16.524 0.000 10328.009 0.000 Inf -0.002 0.999 RaceWhite 0.388 1.474 1.108 0.168 12.925 0.350 0.726 Purity 0.342 1.407 1.104 0.162 12.238 0.309 0.757 Rsquare = 0.047 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.91e-01 Wald test p = 6.27e-01 Score (logrank) test p = 5.31e-01 MVK in UCEC (n=545): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK 0.376 1.457 0.268 0.862 2.463 1.404 0.160 Age 0.048 1.049 0.016 1.016 1.083 2.958 0.003 ** RaceBlack -0.181 0.834 0.812 0.170 4.099 -0.223 0.824 RaceWhite -0.352 0.704 0.758 0.159 3.109 -0.464 0.643 Purity 0.428 1.535 0.645 0.434 5.433 0.664 0.506 Rsquare = 0.045 (max possible = 7.81e-01 ) Likelihood ratio test p = 2.35e-02 Wald test p = 3.15e-02 Score (logrank) test p = 2.92e-02 MVK in UCS (n=57): Model: Surv(OS, EVENT) ~ `MVK` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.197 0.821 0.379 0.391 1.727 -0.519 0.604 Age 0.042 1.043 0.024 0.995 1.093 1.745 0.081 · RaceBlack 17.574 42868128.994 6488.529 0.000 Inf 0.003 0.998 RaceWhite 17.822 54955957.509 6488.529 0.000 Inf 0.003 0.998 Purity -0.746 0.474 1.090 0.056 4.022 -0.684 0.494 Rsquare = 0.124 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.31e-01 Wald test p = 3.28e-01 Score (logrank) test p = 2.42e-01 MVK in UVM (n=80): Model: Surv(OS, EVENT) ~ `MVK` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif MVK -0.540 0.583 0.831 0.114 2.969 -0.650 0.516 Age 0.039 1.040 0.019 1.002 1.080 2.054 0.040 * Gendermale 0.291 1.338 0.485 0.518 3.459 0.601 0.548 Stage3 0.320 1.377 0.507 0.509 3.723 0.631 0.528 Stage4 3.780 43.800 1.215 4.045 474.296 3.110 0.002 ** Purity 1.845 6.328 1.256 0.540 74.136 1.469 0.142 Rsquare = 0.257 (max possible = 8.72e-01 ) Likelihood ratio test p = 8.44e-04 Wald test p = 3.17e-03 Score (logrank) test p = 2.35e-09 PMVK in ACC (n=79): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.254 1.290 0.260 0.775 2.146 0.979 0.328 Age 0.005 1.005 0.013 0.979 1.032 0.352 0.725 Gendermale 0.477 1.612 0.430 0.694 3.746 1.110 0.267 RaceBlack 0.107 1.113 12132.079 0.000 Inf 0.000 1.000 RaceWhite 17.038 25102213.624 10352.144 0.000 Inf 0.002 0.999 Purity 3.167 23.744 2.272 0.277 2038.133 1.394 0.163 Rsquare = 0.08 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.98e-01 Wald test p = 7.81e-01 Score (logrank) test p = 6.17e-01 PMVK in BLCA (n=408): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.004 1.004 0.128 0.781 1.290 0.032 0.974 Age 0.033 1.034 0.009 1.017 1.052 3.879 0.000 *** Gendermale -0.172 0.842 0.180 0.591 1.198 -0.955 0.339 RaceBlack 0.712 2.037 0.447 0.848 4.896 1.591 0.112 RaceWhite 0.117 1.124 0.355 0.561 2.252 0.329 0.742 Stage2 14.507 1997604.466 1861.817 0.000 Inf 0.008 0.994 Stage3 14.942 3085328.283 1861.817 0.000 Inf 0.008 0.994 Stage4 15.482 5294909.013 1861.817 0.000 Inf 0.008 0.993 Purity 0.140 1.151 0.342 0.589 2.247 0.411 0.681 Rsquare = 0.13 (max possible = 9.91e-01 ) Likelihood ratio test p = 2.02e-07 Wald test p = 1.23e-06 Score (logrank) test p = 3.4e-07 PMVK in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.369 0.691 0.137 0.529 0.904 -2.698 0.007 ** Age 0.038 1.039 0.008 1.023 1.054 5.028 0.000 *** Gendermale 0.108 1.114 1.008 0.155 8.034 0.107 0.915 RaceBlack 0.043 1.044 0.619 0.310 3.514 0.070 0.944 RaceWhite -0.253 0.777 0.597 0.241 2.501 -0.423 0.672 Stage2 0.379 1.460 0.305 0.804 2.654 1.243 0.214 Stage3 1.218 3.380 0.313 1.829 6.247 3.886 0.000 *** Stage4 2.655 14.224 0.392 6.601 30.649 6.778 0.000 *** Purity 0.603 1.827 0.420 0.802 4.159 1.436 0.151 Rsquare = 0.088 (max possible = 7.85e-01 ) Likelihood ratio test p = 9.85e-14 Wald test p = 1.98e-17 Score (logrank) test p = 3.04e-23 PMVK in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.125 1.133000e+00 0.361 0.558 2.300 0.345 0.730 Age 0.010 1.010000e+00 0.017 0.976 1.045 0.582 0.561 RaceBlack -0.963 3.820000e-01 1.117 0.043 3.410 -0.862 0.389 RaceWhite -1.241 2.890000e-01 1.118 0.032 2.587 -1.110 0.267 Stage2 18.693 1.313258e+08 6475.386 0.000 Inf 0.003 0.998 Stage3 20.087 5.291521e+08 6475.386 0.000 Inf 0.003 0.998 Stage4 21.414 1.994270e+09 6475.386 0.000 Inf 0.003 0.997 Purity 0.761 2.141000e+00 0.954 0.330 13.884 0.798 0.425 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.97e-04 Wald test p = 6.7e-03 Score (logrank) test p = 4.45e-06 PMVK in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.319 1.376000e+00 0.673 0.368 5.145 0.475 0.635 Age 0.032 1.032000e+00 0.028 0.977 1.091 1.126 0.260 RaceBlack -2.876 5.600000e-02 1.858 0.001 2.149 -1.548 0.122 RaceWhite -1.330 2.650000e-01 1.691 0.010 7.273 -0.786 0.432 Stage2 17.181 2.895780e+07 9653.077 0.000 Inf 0.002 0.999 Stage3 18.832 1.508232e+08 9653.077 0.000 Inf 0.002 0.998 Stage4 52.199 4.674109e+22 2846010.057 0.000 Inf 0.000 1.000 Purity 2.835 1.703300e+01 2.315 0.182 1592.633 1.225 0.221 Rsquare = 0.372 (max possible = 6.68e-01 ) Likelihood ratio test p = 5.86e-04 Wald test p = 1e+00 Score (logrank) test p = 3.03e-14 PMVK in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.543 0.581 0.224 0.375 0.901 -2.426 0.015 * Age 0.049 1.050 0.012 1.026 1.075 4.146 0.000 *** Gendermale -15.331 0.000 3498.803 0.000 Inf -0.004 0.997 RaceBlack -0.317 0.728 1.173 0.073 7.263 -0.270 0.787 RaceWhite 0.316 1.372 1.032 0.182 10.372 0.307 0.759 Stage2 0.322 1.380 0.375 0.661 2.882 0.859 0.391 Stage3 0.896 2.450 0.393 1.135 5.292 2.281 0.023 * Stage4 1.994 7.344 0.594 2.292 23.532 3.356 0.001 ** Purity 0.472 1.603 0.610 0.485 5.296 0.773 0.439 Rsquare = 0.081 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.08e-05 Wald test p = 1.25e-06 Score (logrank) test p = 1.87e-08 PMVK in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.481 0.618 0.285 0.354 1.081 -1.688 0.091 · Age 0.055 1.056 0.021 1.015 1.100 2.663 0.008 ** Gendermale 0.981 2.667 1.109 0.303 23.433 0.884 0.376 RaceBlack 16.731 18458625.703 6868.225 0.000 Inf 0.002 0.998 RaceWhite 15.910 8122455.469 6868.225 0.000 Inf 0.002 0.998 Stage2 0.709 2.031 1.069 0.250 16.497 0.663 0.507 Stage3 1.686 5.396 1.060 0.676 43.107 1.590 0.112 Stage4 2.564 12.987 1.205 1.224 137.809 2.128 0.033 * Purity 0.928 2.528 1.331 0.186 34.304 0.697 0.486 Rsquare = 0.121 (max possible = 6.98e-01 ) Likelihood ratio test p = 1.65e-02 Wald test p = 3.22e-02 Score (logrank) test p = 1.07e-02 PMVK in CESC (n=306): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.114 1.120 0.221 0.726 1.728 0.514 0.607 Age 0.012 1.012 0.010 0.992 1.032 1.194 0.233 RaceBlack 1.046 2.846 1.068 0.351 23.086 0.979 0.327 RaceWhite 0.841 2.319 1.016 0.317 16.981 0.828 0.408 Purity 0.544 1.722 0.738 0.406 7.312 0.737 0.461 Rsquare = 0.015 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.39e-01 Wald test p = 6.73e-01 Score (logrank) test p = 6.64e-01 PMVK in CHOL (n=36): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.961 0.382 0.457 0.156 0.936 -2.104 0.035 * Age 0.025 1.025 0.025 0.976 1.076 0.997 0.319 Gendermale 0.670 1.955 0.586 0.620 6.165 1.144 0.253 RaceBlack 0.604 1.829 1.555 0.087 38.534 0.388 0.698 RaceWhite -0.366 0.693 0.965 0.105 4.595 -0.379 0.704 Stage2 1.329 3.779 0.718 0.925 15.446 1.851 0.064 · Stage3 -15.296 0.000 7359.839 0.000 Inf -0.002 0.998 Stage4 1.557 4.742 0.710 1.180 19.053 2.194 0.028 * Purity 2.733 15.380 1.729 0.519 455.511 1.581 0.114 Rsquare = 0.311 (max possible = 9.46e-01 ) Likelihood ratio test p = 1.45e-01 Wald test p = 3.13e-01 Score (logrank) test p = 1.46e-01 PMVK in COAD (n=458): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.138 0.871 0.304 0.480 1.581 -0.453 0.650 Age 0.024 1.024 0.011 1.001 1.047 2.081 0.037 * Gendermale 0.213 1.237 0.268 0.731 2.094 0.793 0.428 RaceBlack -0.409 0.664 0.826 0.132 3.354 -0.495 0.621 RaceWhite -0.428 0.652 0.774 0.143 2.969 -0.554 0.580 Stage2 0.195 1.216 0.563 0.403 3.664 0.347 0.729 Stage3 0.809 2.246 0.549 0.765 6.593 1.473 0.141 Stage4 1.890 6.618 0.553 2.239 19.562 3.417 0.001 ** Purity -0.177 0.838 0.605 0.256 2.745 -0.292 0.770 Rsquare = 0.11 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.29e-04 Wald test p = 1.42e-04 Score (logrank) test p = 2.05e-05 PMVK in DLBC (n=48): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -2.801 0.061 1.556 0.003 1.281 -1.801 0.072 · Age -0.007 0.993 0.059 0.884 1.114 -0.125 0.901 Gendermale 0.742 2.101 1.110 0.239 18.505 0.669 0.504 RaceBlack 1.225 3.406 2.075 0.058 198.862 0.591 0.555 RaceWhite -2.608 0.074 1.611 0.003 1.733 -1.619 0.106 Purity -3.262 0.038 3.231 0.000 21.554 -1.010 0.313 Rsquare = 0.247 (max possible = 5.58e-01 ) Likelihood ratio test p = 7.03e-02 Wald test p = 3.75e-01 Score (logrank) test p = 1.37e-01 PMVK in ESCA (n=185): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.206 1.229 0.304 0.677 2.231 0.678 0.498 Age 0.011 1.011 0.014 0.983 1.039 0.752 0.452 Gendermale 0.425 1.529 0.545 0.526 4.449 0.780 0.436 RaceBlack 0.295 1.344 1.069 0.165 10.931 0.276 0.782 RaceWhite -0.119 0.888 0.451 0.367 2.149 -0.264 0.792 Stage2 0.793 2.210 0.671 0.593 8.235 1.181 0.237 Stage3 1.596 4.933 0.706 1.237 19.672 2.261 0.024 * Stage4 3.034 20.779 0.819 4.171 103.513 3.703 0.000 *** Purity 0.124 1.132 0.768 0.251 5.103 0.161 0.872 Rsquare = 0.144 (max possible = 9.32e-01 ) Likelihood ratio test p = 9.79e-03 Wald test p = 4.69e-03 Score (logrank) test p = 3.74e-04 PMVK in GBM (n=153): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.150 1.162 0.194 0.795 1.699 0.777 0.437 Age 0.029 1.030 0.008 1.013 1.047 3.566 0.000 *** Gendermale -0.092 0.912 0.213 0.601 1.385 -0.431 0.667 RaceBlack 0.465 1.593 0.732 0.379 6.687 0.636 0.525 RaceWhite -0.307 0.735 0.621 0.218 2.484 -0.495 0.621 Purity -1.113 0.329 0.535 0.115 0.937 -2.082 0.037 * Rsquare = 0.133 (max possible = 9.98e-01 ) Likelihood ratio test p = 3.77e-03 Wald test p = 5.36e-03 Score (logrank) test p = 4.53e-03 PMVK in HNSC (n=522): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.189 0.828 0.138 0.632 1.085 -1.368 0.171 Age 0.021 1.021 0.008 1.006 1.037 2.749 0.006 ** Gendermale -0.251 0.778 0.172 0.556 1.090 -1.460 0.144 RaceBlack 0.179 1.196 0.560 0.400 3.582 0.320 0.749 RaceWhite -0.197 0.821 0.512 0.301 2.239 -0.386 0.700 Stage2 0.583 1.791 0.544 0.616 5.207 1.071 0.284 Stage3 0.836 2.306 0.537 0.805 6.604 1.557 0.120 Stage4 1.232 3.428 0.510 1.261 9.321 2.414 0.016 * Purity -0.017 0.984 0.362 0.484 2.000 -0.046 0.964 Rsquare = 0.074 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.5e-04 Wald test p = 6.54e-04 Score (logrank) test p = 4.69e-04 PMVK in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.286 7.510000e-01 0.383 0.355 1.591 -0.748 0.455 Age 0.008 1.008000e+00 0.025 0.959 1.059 0.314 0.754 Gendermale -0.189 8.280000e-01 0.546 0.284 2.416 -0.346 0.729 RaceBlack 18.989 1.765664e+08 12116.657 0.000 Inf 0.002 0.999 RaceWhite 18.288 8.755143e+07 12116.657 0.000 Inf 0.002 0.999 Stage2 17.319 3.322400e+07 5292.916 0.000 Inf 0.003 0.997 Stage3 16.561 1.556515e+07 5292.916 0.000 Inf 0.003 0.998 Stage4 17.393 3.577478e+07 5292.916 0.000 Inf 0.003 0.997 Purity -1.500 2.230000e-01 1.077 0.027 1.842 -1.393 0.164 Rsquare = 0.095 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.9e-01 Wald test p = 9.33e-01 Score (logrank) test p = 8.31e-01 PMVK in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.174 0.840 0.149 0.628 1.124 -1.174 0.240 Age 0.026 1.027 0.008 1.010 1.044 3.109 0.002 ** Gendermale -0.289 0.749 0.183 0.524 1.072 -1.581 0.114 RaceBlack 0.020 1.020 0.565 0.337 3.085 0.035 0.972 RaceWhite -0.363 0.696 0.513 0.255 1.902 -0.707 0.480 Stage2 0.342 1.408 0.554 0.475 4.173 0.617 0.537 Stage3 0.719 2.053 0.541 0.711 5.925 1.330 0.184 Stage4 1.134 3.107 0.512 1.139 8.480 2.213 0.027 * Purity 0.223 1.250 0.397 0.574 2.723 0.562 0.574 Rsquare = 0.088 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.96e-04 Wald test p = 5.57e-04 Score (logrank) test p = 4.16e-04 PMVK in KICH (n=66): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z PMVK 0.424 1.528000e+00 0.558 5.120000e-01 4.564000e+00 0.760 Age 0.067 1.069000e+00 0.029 1.011000e+00 1.131000e+00 2.327 Gendermale -0.982 3.740000e-01 0.729 9.000000e-02 1.563000e+00 -1.347 RaceBlack -17.841 0.000000e+00 11135.729 0.000000e+00 Inf -0.002 RaceWhite -1.272 2.800000e-01 1.161 2.900000e-02 2.727000e+00 -1.096 Stage2 16.912 2.211613e+07 0.848 4.197807e+06 1.165188e+08 19.947 Stage3 17.978 6.421056e+07 0.780 1.391975e+07 2.961975e+08 23.047 Stage4 20.571 8.590695e+08 0.906 1.455899e+08 5.069034e+09 22.714 Purity 0.953 2.594000e+00 3.828 1.000000e-03 4.705457e+03 0.249 p signif PMVK 0.447 Age 0.020 * Gendermale 0.178 RaceBlack 0.999 RaceWhite 0.273 Stage2 0.000 *** Stage3 0.000 *** Stage4 0.000 *** Purity 0.803 Rsquare = 0.349 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.4e-03 Wald test p = 1.62e-307 Score (logrank) test p = 6.74e-09 PMVK in KIRC (n=533): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.420 0.657 0.171 0.469 0.919 -2.451 0.014 * Age 0.035 1.035 0.008 1.018 1.052 4.137 0.000 *** Gendermale -0.110 0.896 0.185 0.623 1.288 -0.594 0.552 RaceBlack 0.313 1.367 1.056 0.173 10.825 0.296 0.767 RaceWhite 0.198 1.219 1.014 0.167 8.888 0.195 0.845 Stage2 0.247 1.280 0.344 0.652 2.513 0.717 0.474 Stage3 0.721 2.057 0.233 1.302 3.250 3.091 0.002 ** Stage4 1.707 5.514 0.216 3.607 8.429 7.886 0.000 *** Purity 0.192 1.212 0.375 0.582 2.525 0.513 0.608 Rsquare = 0.185 (max possible = 9.65e-01 ) Likelihood ratio test p = 6.91e-16 Wald test p = 3.9e-16 Score (logrank) test p = 2.23e-19 PMVK in KIRP (n=290): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.573 0.564 0.375 0.270 1.176 -1.527 0.127 Age 0.010 1.010 0.016 0.979 1.041 0.616 0.538 Gendermale -0.571 0.565 0.382 0.267 1.195 -1.494 0.135 RaceBlack -1.869 0.154 1.189 0.015 1.587 -1.572 0.116 RaceWhite -1.931 0.145 1.173 0.015 1.446 -1.646 0.100 Stage2 -0.384 0.681 1.057 0.086 5.406 -0.363 0.717 Stage3 1.578 4.847 0.428 2.097 11.206 3.692 0.000 *** Stage4 2.660 14.292 0.507 5.288 38.626 5.243 0.000 *** Purity -0.316 0.729 0.737 0.172 3.088 -0.429 0.668 Rsquare = 0.172 (max possible = 7.58e-01 ) Likelihood ratio test p = 4.42e-06 Wald test p = 3.13e-06 Score (logrank) test p = 3.62e-10 PMVK in LAML (n=173): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.730 2.074 0.278 1.204 3.574 2.629 0.009 ** Age 0.036 1.037 0.008 1.021 1.053 4.539 0.000 *** Gendermale -0.003 0.997 0.218 0.650 1.527 -0.015 0.988 RaceBlack -0.407 0.666 1.106 0.076 5.813 -0.368 0.713 RaceWhite -0.833 0.435 1.020 0.059 3.209 -0.817 0.414 Rsquare = 0.193 (max possible = 9.96e-01 ) Likelihood ratio test p = 6.08e-06 Wald test p = 1.11e-05 Score (logrank) test p = 9.1e-06 PMVK in LGG (n=516): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.844 0.430 0.262 0.257 0.719 -3.221 0.001 ** Age 0.065 1.067 0.008 1.051 1.083 8.455 0.000 *** Gendermale 0.133 1.142 0.196 0.778 1.676 0.678 0.498 RaceBlack 15.451 5134511.904 2113.587 0.000 Inf 0.007 0.994 RaceWhite 15.249 4193670.089 2113.587 0.000 Inf 0.007 0.994 Purity -1.055 0.348 0.403 0.158 0.768 -2.615 0.009 ** Rsquare = 0.156 (max possible = 9.07e-01 ) Likelihood ratio test p = 8.13e-15 Wald test p = 7.55e-15 Score (logrank) test p = 3.66e-16 PMVK in LIHC (n=371): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.079 1.083 0.172 0.773 1.516 0.462 0.644 Age 0.010 1.010 0.008 0.994 1.027 1.265 0.206 Gendermale -0.154 0.857 0.227 0.549 1.338 -0.678 0.497 RaceBlack 0.897 2.452 0.489 0.940 6.396 1.834 0.067 · RaceWhite 0.019 1.019 0.239 0.637 1.629 0.079 0.937 Stage2 0.300 1.350 0.263 0.807 2.258 1.142 0.254 Stage3 0.945 2.572 0.235 1.624 4.074 4.026 0.000 *** Stage4 1.518 4.565 0.638 1.308 15.929 2.381 0.017 * Purity 0.521 1.684 0.474 0.665 4.262 1.100 0.271 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.11e-03 Wald test p = 6.71e-04 Score (logrank) test p = 2.53e-04 PMVK in LUAD (n=515): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.016 0.984 0.134 0.757 1.278 -0.123 0.902 Age 0.007 1.007 0.009 0.989 1.025 0.775 0.438 Gendermale 0.017 1.017 0.169 0.730 1.416 0.100 0.920 RaceBlack 16.069 9516400.898 1883.751 0.000 Inf 0.009 0.993 RaceWhite 16.254 11452887.522 1883.751 0.000 Inf 0.009 0.993 Stage2 0.864 2.372 0.201 1.598 3.519 4.289 0.000 *** Stage3 1.012 2.750 0.219 1.792 4.222 4.626 0.000 *** Stage4 1.006 2.735 0.334 1.421 5.263 3.012 0.003 ** Purity 0.600 1.822 0.347 0.923 3.596 1.728 0.084 · Rsquare = 0.097 (max possible = 9.74e-01 ) Likelihood ratio test p = 2.38e-06 Wald test p = 3.04e-05 Score (logrank) test p = 3.55e-06 PMVK in LUSC (n=501): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.080 0.923 0.141 0.700 1.218 -0.563 0.574 Age 0.017 1.017 0.009 0.998 1.035 1.768 0.077 · Gendermale 0.438 1.550 0.193 1.061 2.265 2.266 0.023 * RaceBlack 0.006 1.006 0.609 0.305 3.321 0.009 0.992 RaceWhite -0.533 0.587 0.567 0.193 1.785 -0.939 0.348 Stage2 0.208 1.232 0.187 0.854 1.776 1.116 0.264 Stage3 0.608 1.836 0.214 1.206 2.794 2.835 0.005 ** Stage4 0.786 2.194 0.801 0.456 10.548 0.981 0.327 Purity -0.319 0.727 0.369 0.353 1.498 -0.865 0.387 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.18e-02 Wald test p = 1.63e-02 Score (logrank) test p = 1.39e-02 PMVK in MESO (n=87): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.510 0.600 0.271 0.353 1.020 -1.885 0.059 · Age 0.020 1.020 0.016 0.989 1.052 1.279 0.201 Gendermale -0.208 0.812 0.327 0.428 1.543 -0.635 0.525 RaceBlack -0.048 0.953 1.528 0.048 19.034 -0.032 0.975 RaceWhite -0.908 0.403 1.073 0.049 3.303 -0.846 0.397 Stage2 -0.090 0.914 0.473 0.362 2.307 -0.191 0.848 Stage3 -0.091 0.913 0.419 0.401 2.076 -0.218 0.828 Stage4 -0.124 0.883 0.474 0.349 2.236 -0.262 0.793 Purity -0.694 0.499 0.531 0.176 1.415 -1.307 0.191 Rsquare = 0.099 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.48e-01 Wald test p = 4.37e-01 Score (logrank) test p = 4.22e-01 PMVK in OV (n=303): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.390 0.677 0.136 0.519 0.883 -2.878 0.004 ** Age 0.035 1.035 0.008 1.019 1.052 4.276 0.000 *** RaceBlack -0.085 0.919 0.576 0.297 2.843 -0.147 0.883 RaceWhite -0.227 0.797 0.516 0.290 2.188 -0.441 0.659 Purity -0.506 0.603 0.661 0.165 2.201 -0.766 0.444 Rsquare = 0.112 (max possible = 9.97e-01 ) Likelihood ratio test p = 3.21e-05 Wald test p = 2.1e-05 Score (logrank) test p = 1.53e-05 PMVK in PAAD (n=179): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.208 0.812 0.181 0.569 1.158 -1.149 0.251 Age 0.023 1.023 0.011 1.002 1.046 2.124 0.034 * Gendermale -0.202 0.817 0.217 0.534 1.250 -0.931 0.352 RaceBlack 0.045 1.046 0.740 0.245 4.463 0.061 0.952 RaceWhite 0.351 1.420 0.476 0.558 3.612 0.736 0.462 Stage2 0.561 1.752 0.441 0.738 4.160 1.271 0.204 Stage3 -0.384 0.681 1.099 0.079 5.873 -0.350 0.727 Stage4 0.232 1.261 0.824 0.251 6.339 0.282 0.778 Purity -0.591 0.554 0.409 0.248 1.235 -1.445 0.149 Rsquare = 0.096 (max possible = 9.91e-01 ) Likelihood ratio test p = 5.53e-02 Wald test p = 7.9e-02 Score (logrank) test p = 7.24e-02 PMVK in PCPG (n=181): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.181 1.198 0.875 0.216 6.657 0.207 0.836 Age 0.039 1.040 0.029 0.982 1.100 1.343 0.179 Gendermale 1.389 4.011 0.902 0.685 23.478 1.541 0.123 RaceBlack -0.185 0.831 19563.173 0.000 Inf 0.000 1.000 RaceWhite 17.261 31359614.632 15635.137 0.000 Inf 0.001 0.999 Purity 5.664 288.432 3.407 0.363 229061.116 1.663 0.096 · Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.6e-01 Wald test p = 4.21e-01 Score (logrank) test p = 3.11e-01 PMVK in PRAD (n=498): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.141 1.151 0.801 0.240 5.529 0.176 0.860 Age 0.008 1.008 0.058 0.900 1.128 0.130 0.897 RaceBlack 15.029 3364756.320 6719.536 0.000 Inf 0.002 0.998 RaceWhite 16.276 11714566.966 6719.536 0.000 Inf 0.002 0.998 Purity 1.084 2.955 1.399 0.191 45.834 0.775 0.439 Rsquare = 0.007 (max possible = 1.83e-01 ) Likelihood ratio test p = 7.4e-01 Wald test p = 8.63e-01 Score (logrank) test p = 8.11e-01 PMVK in READ (n=166): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 1.014 2.756 0.718 0.675 11.251 1.413 0.158 Age 0.130 1.138 0.047 1.038 1.249 2.743 0.006 ** Gendermale -0.626 0.535 0.709 0.133 2.146 -0.883 0.377 RaceBlack 14.143 1387406.326 10593.832 0.000 Inf 0.001 0.999 RaceWhite 12.313 222674.916 10593.832 0.000 Inf 0.001 0.999 Stage2 -2.126 0.119 1.287 0.010 1.488 -1.651 0.099 · Stage3 -0.612 0.542 0.939 0.086 3.415 -0.652 0.514 Stage4 -0.661 0.516 1.028 0.069 3.869 -0.643 0.520 Purity 0.300 1.349 1.302 0.105 17.296 0.230 0.818 Rsquare = 0.232 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.77e-02 Wald test p = 1.66e-01 Score (logrank) test p = 3.48e-02 PMVK in SARC (n=260): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.172 0.842 0.172 0.601 1.179 -1.001 0.317 Age 0.022 1.022 0.008 1.006 1.039 2.651 0.008 ** Gendermale -0.066 0.936 0.230 0.596 1.469 -0.288 0.773 RaceBlack -0.064 0.938 1.088 0.111 7.911 -0.059 0.953 RaceWhite -0.390 0.677 1.025 0.091 5.049 -0.380 0.704 Purity 1.024 2.784 0.587 0.881 8.792 1.745 0.081 · Rsquare = 0.047 (max possible = 9.75e-01 ) Likelihood ratio test p = 8.46e-02 Wald test p = 1.21e-01 Score (logrank) test p = 1.21e-01 PMVK in SKCM (n=471): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.021 1.021 0.131 0.790 1.320 0.159 0.874 Age 0.018 1.018 0.005 1.008 1.029 3.518 0.000 *** Gendermale -0.052 0.949 0.158 0.697 1.293 -0.330 0.742 RaceWhite -1.285 0.277 0.401 0.126 0.608 -3.199 0.001 ** Stage2 0.272 1.312 0.219 0.854 2.017 1.238 0.216 Stage3 0.609 1.839 0.204 1.232 2.745 2.981 0.003 ** Stage4 1.349 3.853 0.352 1.933 7.677 3.834 0.000 *** Purity 1.015 2.758 0.341 1.414 5.382 2.975 0.003 ** Rsquare = 0.123 (max possible = 9.92e-01 ) Likelihood ratio test p = 2.21e-08 Wald test p = 1.17e-08 Score (logrank) test p = 1.41e-09 PMVK in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.004 9.960000e-01 0.365 0.487 2.039 -0.010 0.992 Age 0.012 1.012000e+00 0.017 0.980 1.045 0.723 0.470 Gendermale 0.213 1.237000e+00 0.435 0.528 2.900 0.489 0.625 RaceWhite -1.263 2.830000e-01 0.621 0.084 0.955 -2.033 0.042 * Stage2 17.464 3.841572e+07 6201.071 0.000 Inf 0.003 0.998 Stage3 17.966 6.349657e+07 6201.071 0.000 Inf 0.003 0.998 Stage4 20.086 5.288012e+08 6201.071 0.000 Inf 0.003 0.997 Purity 0.272 1.313000e+00 0.947 0.205 8.403 0.288 0.774 Rsquare = 0.146 (max possible = 8.69e-01 ) Likelihood ratio test p = 6.14e-02 Wald test p = 5.51e-02 Score (logrank) test p = 4.52e-03 PMVK in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.071 1.073 0.140 0.815 1.414 0.504 0.614 Age 0.020 1.020 0.006 1.009 1.032 3.602 0.000 *** Gendermale -0.065 0.937 0.173 0.668 1.315 -0.374 0.708 RaceWhite -1.042 0.353 0.600 0.109 1.145 -1.735 0.083 · Stage2 0.139 1.149 0.232 0.730 1.810 0.601 0.548 Stage3 0.557 1.746 0.209 1.159 2.630 2.664 0.008 ** Stage4 1.125 3.080 0.400 1.407 6.743 2.814 0.005 ** Purity 1.125 3.079 0.371 1.488 6.372 3.032 0.002 ** Rsquare = 0.134 (max possible = 9.95e-01 ) Likelihood ratio test p = 9.88e-07 Wald test p = 1.51e-06 Score (logrank) test p = 5.87e-07 PMVK in STAD (n=415): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.082 0.921 0.201 0.621 1.366 -0.409 0.683 Age 0.026 1.026 0.010 1.006 1.047 2.553 0.011 * Gendermale 0.119 1.126 0.208 0.750 1.692 0.574 0.566 RaceBlack 0.261 1.299 0.447 0.541 3.120 0.585 0.559 RaceWhite 0.103 1.108 0.245 0.686 1.791 0.419 0.675 Stage2 0.478 1.613 0.390 0.750 3.466 1.224 0.221 Stage3 0.902 2.464 0.366 1.203 5.046 2.466 0.014 * Stage4 1.295 3.650 0.508 1.348 9.882 2.548 0.011 * Purity -0.550 0.577 0.381 0.274 1.217 -1.444 0.149 Rsquare = 0.07 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.29e-02 Wald test p = 1.81e-02 Score (logrank) test p = 1.46e-02 PMVK in TGCT (n=150): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p PMVK 110.338 8.300296e+47 88706.920 0 Inf 0.001 0.999 Age -1.793 1.660000e-01 1704.309 0 Inf -0.001 0.999 RaceBlack 98.313 4.976374e+42 18586029.293 0 Inf 0.000 1.000 RaceWhite -199.408 0.000000e+00 17645354.271 0 Inf 0.000 1.000 Stage2 53.705 2.107880e+23 41292.206 0 Inf 0.001 0.999 Stage3 51.399 2.099555e+22 127712.486 0 Inf 0.000 1.000 Purity -316.961 0.000000e+00 239848.388 0 Inf -0.001 0.999 signif PMVK Age RaceBlack RaceWhite Stage2 Stage3 Purity Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.17e-03 PMVK in THCA (n=509): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -1.153 0.316 0.730 0.075 1.320 -1.579 0.114 Age 0.159 1.172 0.030 1.105 1.244 5.251 0.000 *** Gendermale -0.183 0.833 0.606 0.254 2.730 -0.302 0.763 RaceBlack 17.884 58494036.797 9210.590 0.000 Inf 0.002 0.998 RaceWhite 17.393 35786650.886 9210.590 0.000 Inf 0.002 0.998 Stage2 0.457 1.579 1.133 0.171 14.553 0.403 0.687 Stage3 0.130 1.139 0.864 0.209 6.199 0.151 0.880 Stage4 1.710 5.528 0.984 0.803 38.042 1.737 0.082 · Purity 2.885 17.910 1.234 1.596 201.003 2.339 0.019 * Rsquare = 0.155 (max possible = 3.47e-01 ) Likelihood ratio test p = 8.08e-11 Wald test p = 3.03e-04 Score (logrank) test p = 1.03e-10 PMVK in THYM (n=120): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 1.442 4.229 0.664 1.151 15.536 2.172 0.030 * Age 0.071 1.073 0.039 0.995 1.158 1.834 0.067 · Gendermale 0.353 1.423 0.849 0.269 7.519 0.415 0.678 RaceBlack -17.010 0.000 11761.243 0.000 Inf -0.001 0.999 RaceWhite -0.100 0.905 1.154 0.094 8.682 -0.086 0.931 Purity 0.168 1.183 1.215 0.109 12.805 0.138 0.890 Rsquare = 0.087 (max possible = 4.51e-01 ) Likelihood ratio test p = 1.14e-01 Wald test p = 2.18e-01 Score (logrank) test p = 1.21e-01 PMVK in UCEC (n=545): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 0.132 1.141 0.219 0.742 1.752 0.600 0.548 Age 0.050 1.051 0.016 1.019 1.084 3.156 0.002 ** RaceBlack -0.502 0.605 0.806 0.125 2.940 -0.623 0.534 RaceWhite -0.610 0.544 0.757 0.123 2.397 -0.805 0.421 Purity 0.481 1.617 0.647 0.455 5.749 0.743 0.457 Rsquare = 0.04 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.37e-02 Wald test p = 5.03e-02 Score (logrank) test p = 5.02e-02 PMVK in UCS (n=57): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK -0.224 0.799 0.289 0.453 1.409 -0.775 0.438 Age 0.043 1.044 0.024 0.996 1.094 1.805 0.071 · RaceBlack 17.699 48610425.635 6501.291 0.000 Inf 0.003 0.998 RaceWhite 17.977 64167429.139 6501.291 0.000 Inf 0.003 0.998 Purity -1.016 0.362 1.075 0.044 2.980 -0.945 0.345 Rsquare = 0.129 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.06e-01 Wald test p = 2.98e-01 Score (logrank) test p = 2.14e-01 PMVK in UVM (n=80): Model: Surv(OS, EVENT) ~ `PMVK` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif PMVK 2.007 7.439 0.620 2.206 25.082 3.236 0.001 ** Age 0.042 1.042 0.020 1.002 1.084 2.077 0.038 * Gendermale 0.055 1.056 0.511 0.388 2.873 0.107 0.915 Stage3 0.166 1.181 0.516 0.429 3.247 0.322 0.747 Stage4 3.363 28.882 1.212 2.686 310.528 2.775 0.006 ** Purity 1.554 4.728 1.267 0.395 56.636 1.226 0.220 Rsquare = 0.358 (max possible = 8.72e-01 ) Likelihood ratio test p = 6.47e-06 Wald test p = 1.03e-04 Score (logrank) test p = 2.59e-11 NSDHL in ACC (n=79): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.125 1.133 0.188 0.784 1.639 0.665 0.506 Age 0.005 1.005 0.014 0.978 1.033 0.375 0.708 Gendermale 0.445 1.561 0.435 0.666 3.660 1.025 0.305 RaceBlack -0.208 0.812 12022.989 0.000 Inf 0.000 1.000 RaceWhite 16.666 17304482.814 10247.324 0.000 Inf 0.002 0.999 Purity 2.735 15.408 2.382 0.144 1643.290 1.148 0.251 Rsquare = 0.073 (max possible = 9.38e-01 ) Likelihood ratio test p = 5.65e-01 Wald test p = 8.62e-01 Score (logrank) test p = 7.09e-01 NSDHL in BLCA (n=408): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.001 1.001 0.149 0.748 1.341 0.009 0.993 Age 0.033 1.034 0.009 1.017 1.052 3.889 0.000 *** Gendermale -0.171 0.842 0.179 0.593 1.197 -0.956 0.339 RaceBlack 0.710 2.035 0.447 0.847 4.886 1.589 0.112 RaceWhite 0.116 1.124 0.355 0.560 2.253 0.328 0.743 Stage2 14.511 2003958.390 1861.427 0.000 Inf 0.008 0.994 Stage3 14.945 3095606.066 1861.427 0.000 Inf 0.008 0.994 Stage4 15.486 5314521.632 1861.427 0.000 Inf 0.008 0.993 Purity 0.142 1.152 0.338 0.594 2.237 0.419 0.675 Rsquare = 0.13 (max possible = 9.91e-01 ) Likelihood ratio test p = 2.02e-07 Wald test p = 1.23e-06 Score (logrank) test p = 3.41e-07 NSDHL in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.142 1.152 0.198 0.782 1.698 0.716 0.474 Age 0.036 1.036 0.008 1.021 1.052 4.705 0.000 *** Gendermale 0.033 1.033 1.007 0.144 7.440 0.033 0.974 RaceBlack -0.007 0.993 0.619 0.295 3.342 -0.011 0.991 RaceWhite -0.200 0.819 0.597 0.254 2.637 -0.335 0.738 Stage2 0.395 1.485 0.304 0.818 2.696 1.298 0.194 Stage3 1.182 3.261 0.313 1.766 6.022 3.777 0.000 *** Stage4 2.435 11.420 0.404 5.174 25.207 6.029 0.000 *** Purity 0.472 1.604 0.427 0.694 3.705 1.105 0.269 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 1.97e-12 Wald test p = 4.23e-16 Score (logrank) test p = 4.47e-22 NSDHL in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.227 7.970000e-01 0.392 0.370 1.717 -0.579 0.563 Age 0.011 1.011000e+00 0.018 0.977 1.047 0.643 0.520 RaceBlack -0.870 4.190000e-01 1.114 0.047 3.719 -0.781 0.435 RaceWhite -1.236 2.910000e-01 1.125 0.032 2.634 -1.099 0.272 Stage2 18.743 1.379666e+08 6489.617 0.000 Inf 0.003 0.998 Stage3 20.144 5.605291e+08 6489.617 0.000 Inf 0.003 0.998 Stage4 21.408 1.982715e+09 6489.617 0.000 Inf 0.003 0.997 Purity 0.692 1.998000e+00 0.962 0.303 13.176 0.719 0.472 Rsquare = 0.158 (max possible = 7.18e-01 ) Likelihood ratio test p = 4.57e-04 Wald test p = 6.06e-03 Score (logrank) test p = 3.76e-06 NSDHL in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.693 1.999000e+00 0.702 0.505 7.909 0.987 0.324 Age 0.017 1.017000e+00 0.032 0.956 1.082 0.534 0.593 RaceBlack -2.704 6.700000e-02 1.823 0.002 2.384 -1.483 0.138 RaceWhite -1.347 2.600000e-01 1.482 0.014 4.750 -0.909 0.364 Stage2 18.339 9.216249e+07 15095.398 0.000 Inf 0.001 0.999 Stage3 20.174 5.771638e+08 15095.398 0.000 Inf 0.001 0.999 Stage4 NA NA 0.000 NA NA NA NA Purity 3.581 3.592000e+01 2.545 0.245 5271.289 1.407 0.159 Rsquare = 0.381 (max possible = 6.68e-01 ) Likelihood ratio test p = 1.97e-04 Wald test p = 2.02e-01 Score (logrank) test p = 7.28e-15 NSDHL in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.132 1.142 0.322 0.608 2.144 0.412 0.680 Age 0.048 1.050 0.012 1.025 1.074 4.078 0.000 *** Gendermale -15.377 0.000 3458.869 0.000 Inf -0.004 0.996 RaceBlack -0.466 0.628 1.176 0.063 6.296 -0.396 0.692 RaceWhite 0.248 1.282 1.032 0.170 9.677 0.240 0.810 Stage2 0.316 1.372 0.375 0.658 2.861 0.842 0.400 Stage3 0.858 2.357 0.394 1.089 5.102 2.177 0.029 * Stage4 2.111 8.260 0.599 2.554 26.712 3.526 0.000 *** Purity 0.248 1.282 0.634 0.370 4.441 0.391 0.696 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 9.73e-05 Wald test p = 1.78e-05 Score (logrank) test p = 3.16e-07 NSDHL in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.309 1.362 0.455 0.558 3.323 0.678 0.498 Age 0.051 1.052 0.021 1.010 1.095 2.451 0.014 * Gendermale 0.965 2.625 1.109 0.299 23.080 0.870 0.384 RaceBlack 16.636 16789839.998 6393.028 0.000 Inf 0.003 0.998 RaceWhite 16.051 9354772.543 6393.028 0.000 Inf 0.003 0.998 Stage2 0.611 1.842 1.079 0.222 15.251 0.566 0.571 Stage3 1.549 4.705 1.063 0.586 37.815 1.457 0.145 Stage4 1.787 5.973 1.253 0.513 69.605 1.427 0.154 Purity 1.026 2.790 1.358 0.195 39.970 0.755 0.450 Rsquare = 0.108 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.63e-02 Wald test p = 6.17e-02 Score (logrank) test p = 2.14e-02 NSDHL in CESC (n=306): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.470 0.625 0.259 0.376 1.039 -1.812 0.070 · Age 0.012 1.012 0.010 0.992 1.032 1.186 0.236 RaceBlack 1.046 2.847 1.069 0.350 23.138 0.979 0.328 RaceWhite 0.791 2.205 1.015 0.302 16.123 0.779 0.436 Purity 0.475 1.607 0.728 0.386 6.689 0.652 0.514 Rsquare = 0.028 (max possible = 8.91e-01 ) Likelihood ratio test p = 2.66e-01 Wald test p = 2.92e-01 Score (logrank) test p = 2.83e-01 NSDHL in CHOL (n=36): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.565 1.760 0.729 0.422 7.342 0.776 0.438 Age 0.019 1.019 0.022 0.976 1.064 0.864 0.388 Gendermale 0.406 1.502 0.605 0.459 4.916 0.672 0.502 RaceBlack -0.174 0.840 1.497 0.045 15.799 -0.116 0.907 RaceWhite -0.976 0.377 0.891 0.066 2.162 -1.095 0.274 Stage2 0.634 1.885 0.675 0.502 7.078 0.940 0.347 Stage3 -14.819 0.000 6973.092 0.000 Inf -0.002 0.998 Stage4 0.869 2.384 0.678 0.632 8.999 1.282 0.200 Purity 2.193 8.964 1.524 0.452 177.806 1.439 0.150 Rsquare = 0.225 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.22e-01 Wald test p = 6.09e-01 Score (logrank) test p = 4.44e-01 NSDHL in COAD (n=458): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.197 1.218 0.247 0.751 1.976 0.798 0.425 Age 0.024 1.025 0.012 1.002 1.048 2.122 0.034 * Gendermale 0.224 1.252 0.268 0.740 2.118 0.836 0.403 RaceBlack -0.344 0.709 0.829 0.140 3.599 -0.415 0.678 RaceWhite -0.394 0.674 0.775 0.148 3.078 -0.509 0.611 Stage2 0.211 1.235 0.562 0.411 3.715 0.376 0.707 Stage3 0.792 2.209 0.551 0.751 6.498 1.439 0.150 Stage4 1.873 6.510 0.553 2.200 19.262 3.385 0.001 ** Purity -0.301 0.740 0.614 0.222 2.468 -0.489 0.625 Rsquare = 0.111 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.63e-04 Wald test p = 1.44e-04 Score (logrank) test p = 2.03e-05 NSDHL in DLBC (n=48): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 2.009 7.456 1.687 0.273 203.621 1.191 0.234 Age -0.030 0.971 0.052 0.877 1.074 -0.574 0.566 Gendermale 2.020 7.542 1.863 0.196 290.856 1.084 0.278 RaceBlack 1.144 3.141 1.714 0.109 90.377 0.668 0.504 RaceWhite -2.780 0.062 1.663 0.002 1.616 -1.671 0.095 · Purity -3.158 0.043 2.326 0.000 4.056 -1.358 0.174 Rsquare = 0.168 (max possible = 5.58e-01 ) Likelihood ratio test p = 2.74e-01 Wald test p = 6.06e-01 Score (logrank) test p = 2.75e-01 NSDHL in ESCA (n=185): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.378 1.460 0.270 0.860 2.477 1.402 0.161 Age 0.011 1.011 0.014 0.984 1.040 0.789 0.430 Gendermale 0.535 1.707 0.542 0.590 4.938 0.986 0.324 RaceBlack 0.133 1.142 1.076 0.139 9.413 0.124 0.902 RaceWhite -0.008 0.992 0.453 0.408 2.411 -0.018 0.986 Stage2 0.652 1.919 0.659 0.527 6.987 0.989 0.323 Stage3 1.376 3.957 0.675 1.054 14.852 2.038 0.042 * Stage4 2.747 15.603 0.779 3.388 71.865 3.526 0.000 *** Purity 0.026 1.026 0.780 0.223 4.734 0.033 0.973 Rsquare = 0.153 (max possible = 9.32e-01 ) Likelihood ratio test p = 5.73e-03 Wald test p = 2.69e-03 Score (logrank) test p = 1.95e-04 NSDHL in GBM (n=153): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.072 0.930 0.324 0.493 1.757 -0.223 0.824 Age 0.030 1.030 0.008 1.014 1.047 3.585 0.000 *** Gendermale -0.092 0.912 0.214 0.600 1.386 -0.430 0.667 RaceBlack 0.546 1.727 0.731 0.412 7.228 0.748 0.455 RaceWhite -0.235 0.791 0.614 0.237 2.635 -0.382 0.702 Purity -1.056 0.348 0.551 0.118 1.024 -1.917 0.055 · Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.71e-03 Wald test p = 7e-03 Score (logrank) test p = 6e-03 NSDHL in HNSC (n=522): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.370 1.447 0.168 1.041 2.013 2.197 0.028 * Age 0.021 1.021 0.008 1.006 1.037 2.795 0.005 ** Gendermale -0.244 0.784 0.172 0.560 1.097 -1.421 0.155 RaceBlack 0.060 1.062 0.559 0.355 3.180 0.108 0.914 RaceWhite -0.271 0.763 0.511 0.280 2.076 -0.530 0.596 Stage2 0.620 1.859 0.544 0.641 5.396 1.141 0.254 Stage3 0.826 2.284 0.536 0.798 6.535 1.540 0.124 Stage4 1.259 3.522 0.510 1.297 9.567 2.469 0.014 * Purity -0.180 0.835 0.375 0.401 1.740 -0.481 0.630 Rsquare = 0.08 (max possible = 9.89e-01 ) Likelihood ratio test p = 7.96e-05 Wald test p = 2.14e-04 Score (logrank) test p = 1.52e-04 NSDHL in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.551 1.735000e+00 0.555 0.585 5.153 0.993 0.321 Age 0.006 1.006000e+00 0.026 0.957 1.058 0.241 0.809 Gendermale -0.067 9.360000e-01 0.553 0.317 2.765 -0.120 0.904 RaceBlack 18.526 1.110901e+08 12187.204 0.000 Inf 0.002 0.999 RaceWhite 17.582 4.321897e+07 12187.204 0.000 Inf 0.001 0.999 Stage2 17.440 3.751546e+07 5383.282 0.000 Inf 0.003 0.997 Stage3 16.478 1.432669e+07 5383.282 0.000 Inf 0.003 0.998 Stage4 17.475 3.886051e+07 5383.282 0.000 Inf 0.003 0.997 Purity -1.748 1.740000e-01 1.082 0.021 1.452 -1.615 0.106 Rsquare = 0.1 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.5e-01 Wald test p = 9.02e-01 Score (logrank) test p = 7.86e-01 NSDHL in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.363 1.437 0.179 1.011 2.042 2.023 0.043 * Age 0.026 1.026 0.008 1.010 1.043 3.110 0.002 ** Gendermale -0.294 0.745 0.183 0.521 1.067 -1.607 0.108 RaceBlack -0.084 0.919 0.565 0.304 2.783 -0.149 0.882 RaceWhite -0.398 0.672 0.512 0.246 1.834 -0.776 0.438 Stage2 0.369 1.446 0.553 0.489 4.279 0.667 0.505 Stage3 0.696 2.005 0.540 0.695 5.782 1.287 0.198 Stage4 1.151 3.161 0.512 1.159 8.616 2.249 0.025 * Purity 0.109 1.115 0.413 0.496 2.503 0.263 0.793 Rsquare = 0.095 (max possible = 9.89e-01 ) Likelihood ratio test p = 6.8e-05 Wald test p = 2.11e-04 Score (logrank) test p = 1.48e-04 NSDHL in KICH (n=66): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p NSDHL 0.606 1.834000e+00 0.736 0.434 7.754000e+00 0.825 0.410 Age 0.076 1.079000e+00 0.029 1.020 1.141000e+00 2.663 0.008 Gendermale -0.941 3.900000e-01 0.728 0.094 1.627000e+00 -1.292 0.196 RaceBlack -16.694 0.000000e+00 6411.861 0.000 Inf -0.003 0.998 RaceWhite -1.473 2.290000e-01 1.158 0.024 2.217000e+00 -1.272 0.203 Stage2 15.841 7.578886e+06 0.852 1427633.099 4.023408e+07 18.599 0.000 Stage3 17.092 2.647070e+07 0.775 5793475.058 1.209460e+08 22.049 0.000 Stage4 19.443 2.779539e+08 0.902 47408416.898 1.629634e+09 21.546 0.000 Purity 0.175 1.192000e+00 3.242 0.002 6.856200e+02 0.054 0.957 signif NSDHL Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.35 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.34e-03 Wald test p = 8e-276 Score (logrank) test p = 9.51e-09 NSDHL in KIRC (n=533): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.810 0.445 0.215 0.292 0.678 -3.766 0.000 *** Age 0.034 1.034 0.009 1.017 1.051 3.935 0.000 *** Gendermale -0.073 0.930 0.184 0.649 1.333 -0.395 0.693 RaceBlack -0.030 0.971 1.058 0.122 7.723 -0.028 0.978 RaceWhite 0.072 1.074 1.014 0.147 7.832 0.071 0.944 Stage2 0.060 1.062 0.347 0.538 2.098 0.174 0.862 Stage3 0.688 1.990 0.234 1.259 3.147 2.943 0.003 ** Stage4 1.689 5.412 0.217 3.539 8.278 7.790 0.000 *** Purity -0.071 0.931 0.355 0.464 1.867 -0.201 0.841 Rsquare = 0.199 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.66e-17 Wald test p = 4.64e-18 Score (logrank) test p = 1.3e-21 NSDHL in KIRP (n=290): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.573 1.774 0.499 0.667 4.716 1.149 0.250 Age 0.010 1.010 0.016 0.979 1.042 0.628 0.530 Gendermale -0.363 0.695 0.397 0.320 1.513 -0.916 0.360 RaceBlack -1.941 0.144 1.196 0.014 1.496 -1.623 0.105 RaceWhite -1.982 0.138 1.177 0.014 1.383 -1.684 0.092 · Stage2 -0.384 0.681 1.055 0.086 5.390 -0.364 0.716 Stage3 1.578 4.847 0.428 2.095 11.218 3.687 0.000 *** Stage4 2.814 16.678 0.522 5.991 46.431 5.387 0.000 *** Purity -0.389 0.678 0.757 0.154 2.987 -0.514 0.607 Rsquare = 0.168 (max possible = 7.58e-01 ) Likelihood ratio test p = 6.61e-06 Wald test p = 3.21e-06 Score (logrank) test p = 4.64e-10 NSDHL in LAML (n=173): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.444 1.559 0.329 0.818 2.971 1.350 0.177 Age 0.039 1.040 0.008 1.023 1.057 4.768 0.000 *** Gendermale -0.138 0.871 0.212 0.575 1.320 -0.650 0.516 RaceBlack -0.403 0.668 1.107 0.076 5.851 -0.364 0.716 RaceWhite -0.741 0.477 1.018 0.065 3.508 -0.727 0.467 Rsquare = 0.166 (max possible = 9.96e-01 ) Likelihood ratio test p = 5.49e-05 Wald test p = 2.27e-04 Score (logrank) test p = 1.48e-04 NSDHL in LGG (n=516): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.341 0.711 0.178 0.501 1.008 -1.914 0.056 · Age 0.063 1.065 0.008 1.049 1.081 8.195 0.000 *** Gendermale 0.030 1.031 0.198 0.699 1.519 0.153 0.879 RaceBlack 15.410 4924616.772 2020.800 0.000 Inf 0.008 0.994 RaceWhite 15.436 5055739.523 2020.800 0.000 Inf 0.008 0.994 Purity -0.866 0.421 0.405 0.190 0.931 -2.136 0.033 * Rsquare = 0.143 (max possible = 9.07e-01 ) Likelihood ratio test p = 2.01e-13 Wald test p = 1.41e-13 Score (logrank) test p = 1.38e-14 NSDHL in LIHC (n=371): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.210 1.233 0.147 0.924 1.647 1.423 0.155 Age 0.009 1.009 0.008 0.993 1.025 1.074 0.283 Gendermale -0.182 0.833 0.229 0.531 1.306 -0.795 0.426 RaceBlack 0.753 2.123 0.498 0.800 5.632 1.512 0.130 RaceWhite 0.024 1.025 0.239 0.641 1.638 0.102 0.919 Stage2 0.305 1.357 0.261 0.814 2.264 1.170 0.242 Stage3 0.885 2.424 0.238 1.519 3.868 3.715 0.000 *** Stage4 1.539 4.661 0.619 1.385 15.683 2.487 0.013 * Purity 0.468 1.597 0.465 0.642 3.973 1.006 0.314 Rsquare = 0.091 (max possible = 9.66e-01 ) Likelihood ratio test p = 5.53e-04 Wald test p = 3.51e-04 Score (logrank) test p = 1.31e-04 NSDHL in LUAD (n=515): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.504 1.656 0.164 1.200 2.285 3.071 0.002 ** Age 0.009 1.009 0.009 0.991 1.026 0.970 0.332 Gendermale 0.033 1.034 0.168 0.743 1.437 0.197 0.844 RaceBlack 16.571 15725934.907 1813.872 0.000 Inf 0.009 0.993 RaceWhite 16.750 18811653.865 1813.872 0.000 Inf 0.009 0.993 Stage2 0.870 2.388 0.202 1.607 3.548 4.310 0.000 *** Stage3 0.945 2.574 0.219 1.675 3.954 4.314 0.000 *** Stage4 1.139 3.124 0.337 1.614 6.044 3.382 0.001 ** Purity 0.707 2.028 0.350 1.021 4.029 2.018 0.044 * Rsquare = 0.116 (max possible = 9.74e-01 ) Likelihood ratio test p = 4.51e-08 Wald test p = 1.12e-06 Score (logrank) test p = 1.2e-07 NSDHL in LUSC (n=501): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.196 0.822 0.165 0.595 1.136 -1.187 0.235 Age 0.017 1.017 0.009 0.998 1.035 1.775 0.076 · Gendermale 0.449 1.566 0.194 1.071 2.290 2.316 0.021 * RaceBlack 0.064 1.066 0.606 0.325 3.494 0.105 0.916 RaceWhite -0.484 0.616 0.561 0.205 1.853 -0.862 0.389 Stage2 0.233 1.262 0.188 0.874 1.822 1.241 0.215 Stage3 0.612 1.845 0.214 1.212 2.809 2.855 0.004 ** Stage4 0.752 2.121 0.791 0.450 9.996 0.951 0.342 Purity -0.232 0.793 0.377 0.379 1.659 -0.617 0.538 Rsquare = 0.054 (max possible = 9.87e-01 ) Likelihood ratio test p = 1.5e-02 Wald test p = 1.21e-02 Score (logrank) test p = 1.07e-02 NSDHL in MESO (n=87): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.209 1.232 0.318 0.661 2.296 0.657 0.511 Age 0.020 1.020 0.016 0.989 1.053 1.273 0.203 Gendermale -0.131 0.877 0.337 0.453 1.699 -0.389 0.697 RaceBlack 0.174 1.190 1.531 0.059 23.899 0.113 0.910 RaceWhite -0.447 0.640 1.050 0.082 5.006 -0.426 0.670 Stage2 -0.186 0.830 0.476 0.326 2.111 -0.391 0.696 Stage3 -0.068 0.935 0.426 0.405 2.156 -0.159 0.874 Stage4 -0.082 0.922 0.490 0.353 2.408 -0.166 0.868 Purity -0.765 0.465 0.560 0.155 1.394 -1.367 0.172 Rsquare = 0.065 (max possible = 9.98e-01 ) Likelihood ratio test p = 7.71e-01 Wald test p = 7.47e-01 Score (logrank) test p = 7.4e-01 NSDHL in OV (n=303): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.017 1.017 0.150 0.759 1.364 0.115 0.908 Age 0.036 1.037 0.008 1.020 1.054 4.279 0.000 *** RaceBlack -0.050 0.951 0.577 0.307 2.948 -0.087 0.931 RaceWhite -0.154 0.858 0.516 0.312 2.357 -0.298 0.766 Purity -0.540 0.583 0.672 0.156 2.175 -0.803 0.422 Rsquare = 0.081 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.15e-03 Wald test p = 9.97e-04 Score (logrank) test p = 8.29e-04 NSDHL in PAAD (n=179): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.134 1.144 0.221 0.742 1.763 0.609 0.542 Age 0.022 1.022 0.011 1.001 1.044 2.023 0.043 * Gendermale -0.220 0.802 0.217 0.525 1.227 -1.016 0.309 RaceBlack 0.019 1.019 0.741 0.238 4.351 0.025 0.980 RaceWhite 0.399 1.491 0.478 0.584 3.803 0.835 0.404 Stage2 0.638 1.892 0.438 0.803 4.460 1.458 0.145 Stage3 -0.217 0.805 1.093 0.095 6.850 -0.199 0.842 Stage4 0.177 1.194 0.830 0.235 6.072 0.214 0.831 Purity -0.665 0.514 0.411 0.230 1.150 -1.620 0.105 Rsquare = 0.091 (max possible = 9.91e-01 ) Likelihood ratio test p = 7.42e-02 Wald test p = 1.02e-01 Score (logrank) test p = 1.02e-01 NSDHL in PCPG (n=181): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.027 1.027 0.760 0.232 4.556 0.035 0.972 Age 0.038 1.038 0.028 0.982 1.098 1.332 0.183 Gendermale 1.397 4.044 0.935 0.647 25.275 1.495 0.135 RaceBlack -0.229 0.795 19650.942 0.000 Inf 0.000 1.000 RaceWhite 17.251 31039235.687 15817.247 0.000 Inf 0.001 0.999 Purity 5.613 274.082 3.397 0.352 213688.616 1.652 0.098 · Rsquare = 0.055 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.62e-01 Wald test p = 4.13e-01 Score (logrank) test p = 3.1e-01 NSDHL in PRAD (n=498): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 1.534 4.637 0.788 0.989 21.742 1.946 0.052 · Age 0.014 1.014 0.057 0.906 1.135 0.238 0.812 RaceBlack 15.352 4649206.402 7539.135 0.000 Inf 0.002 0.998 RaceWhite 16.373 12897917.576 7539.135 0.000 Inf 0.002 0.998 Purity 1.168 3.214 1.473 0.179 57.676 0.793 0.428 Rsquare = 0.015 (max possible = 1.83e-01 ) Likelihood ratio test p = 3.01e-01 Wald test p = 3.21e-01 Score (logrank) test p = 2.73e-01 NSDHL in READ (n=166): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.177 0.838 0.668 0.226 3.102 -0.265 0.791 Age 0.109 1.115 0.044 1.023 1.216 2.475 0.013 * Gendermale -0.337 0.714 0.688 0.185 2.749 -0.490 0.624 RaceBlack 13.337 619421.213 10179.190 0.000 Inf 0.001 0.999 RaceWhite 12.302 220066.332 10179.190 0.000 Inf 0.001 0.999 Stage2 -1.997 0.136 1.372 0.009 1.999 -1.455 0.146 Stage3 -0.585 0.557 0.986 0.081 3.848 -0.594 0.553 Stage4 -0.285 0.752 1.091 0.089 6.373 -0.262 0.794 Purity 0.096 1.101 1.329 0.081 14.901 0.072 0.942 Rsquare = 0.21 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.62e-02 Wald test p = 2.31e-01 Score (logrank) test p = 4.86e-02 NSDHL in SARC (n=260): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.137 1.147 0.181 0.804 1.636 0.757 0.449 Age 0.022 1.023 0.008 1.006 1.039 2.682 0.007 ** Gendermale -0.011 0.989 0.222 0.641 1.527 -0.049 0.961 RaceBlack -0.139 0.870 1.086 0.104 7.314 -0.128 0.898 RaceWhite -0.486 0.615 1.023 0.083 4.569 -0.475 0.635 Purity 0.949 2.584 0.577 0.834 8.007 1.646 0.100 Rsquare = 0.045 (max possible = 9.75e-01 ) Likelihood ratio test p = 9.87e-02 Wald test p = 1.38e-01 Score (logrank) test p = 1.37e-01 NSDHL in SKCM (n=471): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.321 1.379 0.151 1.026 1.852 2.132 0.033 * Age 0.018 1.018 0.005 1.008 1.029 3.464 0.001 ** Gendermale -0.048 0.953 0.157 0.700 1.298 -0.306 0.760 RaceWhite -1.368 0.255 0.404 0.115 0.563 -3.383 0.001 ** Stage2 0.309 1.362 0.219 0.887 2.090 1.411 0.158 Stage3 0.593 1.810 0.204 1.213 2.700 2.905 0.004 ** Stage4 1.401 4.059 0.353 2.032 8.106 3.970 0.000 *** Purity 0.986 2.680 0.339 1.379 5.209 2.908 0.004 ** Rsquare = 0.133 (max possible = 9.92e-01 ) Likelihood ratio test p = 3.07e-09 Wald test p = 1.93e-09 Score (logrank) test p = 2.05e-10 NSDHL in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.149 1.161000e+00 0.362 0.571 2.362 0.412 0.680 Age 0.012 1.012000e+00 0.016 0.981 1.045 0.769 0.442 Gendermale 0.245 1.278000e+00 0.441 0.538 3.033 0.556 0.578 RaceWhite -1.252 2.860000e-01 0.624 0.084 0.972 -2.006 0.045 * Stage2 17.419 3.674299e+07 6215.727 0.000 Inf 0.003 0.998 Stage3 17.898 5.929043e+07 6215.727 0.000 Inf 0.003 0.998 Stage4 20.049 5.096516e+08 6215.727 0.000 Inf 0.003 0.997 Purity 0.244 1.277000e+00 0.953 0.197 8.263 0.256 0.798 Rsquare = 0.148 (max possible = 8.69e-01 ) Likelihood ratio test p = 5.8e-02 Wald test p = 5.04e-02 Score (logrank) test p = 4.1e-03 NSDHL in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.254 1.289 0.170 0.924 1.799 1.495 0.135 Age 0.020 1.020 0.006 1.009 1.032 3.575 0.000 *** Gendermale -0.062 0.940 0.172 0.670 1.318 -0.360 0.719 RaceWhite -1.189 0.304 0.607 0.093 1.001 -1.958 0.050 · Stage2 0.186 1.204 0.231 0.765 1.894 0.803 0.422 Stage3 0.549 1.731 0.209 1.149 2.608 2.626 0.009 ** Stage4 1.174 3.234 0.401 1.474 7.096 2.928 0.003 ** Purity 1.106 3.021 0.369 1.464 6.231 2.992 0.003 ** Rsquare = 0.14 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.23e-07 Wald test p = 6.84e-07 Score (logrank) test p = 2.58e-07 NSDHL in STAD (n=415): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.277 0.758 0.171 0.542 1.060 -1.618 0.106 Age 0.028 1.028 0.010 1.008 1.049 2.735 0.006 ** Gendermale 0.143 1.154 0.208 0.768 1.734 0.691 0.490 RaceBlack 0.385 1.469 0.454 0.604 3.575 0.848 0.396 RaceWhite 0.129 1.138 0.246 0.703 1.841 0.526 0.599 Stage2 0.528 1.695 0.390 0.789 3.642 1.352 0.176 Stage3 0.903 2.468 0.363 1.212 5.025 2.490 0.013 * Stage4 1.371 3.940 0.503 1.471 10.550 2.729 0.006 ** Purity -0.500 0.607 0.379 0.289 1.275 -1.319 0.187 Rsquare = 0.078 (max possible = 9.79e-01 ) Likelihood ratio test p = 5.21e-03 Wald test p = 7.2e-03 Score (logrank) test p = 5.43e-03 NSDHL in TGCT (n=150): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 10.044 2.300882e+04 75026.16 0 Inf 0.000 1.000 Age -1.671 1.880000e-01 1672.80 0 Inf -0.001 0.999 RaceBlack 17.227 3.030297e+07 20160643.12 0 Inf 0.000 1.000 RaceWhite -29.897 0.000000e+00 19482555.92 0 Inf 0.000 1.000 Stage2 -2.917 5.400000e-02 41374.59 0 Inf 0.000 1.000 Stage3 15.219 4.070134e+06 138924.05 0 Inf 0.000 1.000 Purity 27.123 6.016595e+11 229294.45 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.27e-03 NSDHL in THCA (n=509): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL -0.853 0.426 0.921 0.070 2.590 -0.926 0.354 Age 0.150 1.162 0.029 1.097 1.231 5.123 0.000 *** Gendermale 0.041 1.042 0.626 0.305 3.554 0.066 0.948 RaceBlack 16.859 20973652.548 5850.480 0.000 Inf 0.003 0.998 RaceWhite 16.452 13963228.951 5850.480 0.000 Inf 0.003 0.998 Stage2 -0.223 0.800 1.085 0.095 6.714 -0.205 0.837 Stage3 0.067 1.070 0.870 0.194 5.884 0.078 0.938 Stage4 1.589 4.899 0.966 0.737 32.555 1.644 0.100 Purity 2.349 10.476 1.133 1.137 96.503 2.074 0.038 * Rsquare = 0.151 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.75e-10 Wald test p = 5.55e-04 Score (logrank) test p = 1.03e-10 NSDHL in THYM (n=120): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 1.305 3.688 0.647 1.037 13.117 2.016 0.044 * Age 0.063 1.066 0.035 0.995 1.141 1.825 0.068 · Gendermale 0.264 1.302 0.839 0.251 6.740 0.314 0.753 RaceBlack -16.506 0.000 12043.531 0.000 Inf -0.001 0.999 RaceWhite 0.346 1.413 1.119 0.158 12.665 0.309 0.757 Purity 0.278 1.321 1.176 0.132 13.245 0.236 0.813 Rsquare = 0.077 (max possible = 4.51e-01 ) Likelihood ratio test p = 1.7e-01 Wald test p = 2.98e-01 Score (logrank) test p = 1.64e-01 NSDHL in UCEC (n=545): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.355 1.426 0.302 0.789 2.579 1.174 0.240 Age 0.049 1.050 0.016 1.017 1.083 3.044 0.002 ** RaceBlack -0.302 0.740 0.798 0.155 3.530 -0.378 0.705 RaceWhite -0.396 0.673 0.751 0.155 2.932 -0.527 0.598 Purity 0.301 1.351 0.655 0.374 4.881 0.459 0.646 Rsquare = 0.043 (max possible = 7.81e-01 ) Likelihood ratio test p = 2.91e-02 Wald test p = 3.5e-02 Score (logrank) test p = 3.43e-02 NSDHL in UCS (n=57): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.172 1.188 0.479 0.464 3.040 0.359 0.720 Age 0.044 1.045 0.024 0.997 1.096 1.829 0.067 · RaceBlack 17.676 47464875.020 6476.449 0.000 Inf 0.003 0.998 RaceWhite 17.850 56541985.639 6476.449 0.000 Inf 0.003 0.998 Purity -0.878 0.415 1.056 0.052 3.289 -0.832 0.405 Rsquare = 0.121 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.43e-01 Wald test p = 3.5e-01 Score (logrank) test p = 2.56e-01 NSDHL in UVM (n=80): Model: Surv(OS, EVENT) ~ `NSDHL` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif NSDHL 0.882 2.415 0.680 0.637 9.161 1.296 0.195 Age 0.047 1.048 0.020 1.007 1.091 2.297 0.022 * Gendermale 0.205 1.227 0.486 0.473 3.182 0.421 0.674 Stage3 0.243 1.275 0.499 0.480 3.391 0.487 0.626 Stage4 3.629 37.680 1.226 3.406 416.800 2.959 0.003 ** Purity 2.035 7.650 1.249 0.661 88.490 1.629 0.103 Rsquare = 0.269 (max possible = 8.72e-01 ) Likelihood ratio test p = 4.91e-04 Wald test p = 2.41e-03 Score (logrank) test p = 1.57e-09 SQLE in ACC (n=79): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.209 1.233 0.135 0.946 1.606 1.551 0.121 Age 0.009 1.009 0.014 0.982 1.038 0.652 0.514 Gendermale 0.514 1.672 0.429 0.722 3.875 1.199 0.231 RaceBlack -0.603 0.547 12279.632 0.000 Inf 0.000 1.000 RaceWhite 16.282 11776143.354 10482.945 0.000 Inf 0.002 0.999 Purity 2.343 10.415 2.344 0.105 1029.121 1.000 0.317 Rsquare = 0.104 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.17e-01 Wald test p = 6.03e-01 Score (logrank) test p = 4.45e-01 SQLE in BLCA (n=408): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.212 1.236 0.090 1.037 1.475 2.362 0.018 * Age 0.032 1.033 0.009 1.016 1.050 3.766 0.000 *** Gendermale -0.193 0.825 0.180 0.580 1.173 -1.072 0.284 RaceBlack 0.570 1.769 0.451 0.731 4.279 1.265 0.206 RaceWhite 0.137 1.147 0.355 0.572 2.301 0.386 0.700 Stage2 14.564 2113758.962 1831.254 0.000 Inf 0.008 0.994 Stage3 15.027 3358586.401 1831.254 0.000 Inf 0.008 0.993 Stage4 15.506 5420938.256 1831.254 0.000 Inf 0.008 0.993 Purity 0.038 1.039 0.347 0.527 2.050 0.111 0.912 Rsquare = 0.144 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.75e-08 Wald test p = 2.03e-07 Score (logrank) test p = 6.13e-08 SQLE in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.193 1.213 0.083 1.032 1.426 2.335 0.020 * Age 0.038 1.039 0.008 1.024 1.055 5.001 0.000 *** Gendermale -0.030 0.970 1.007 0.135 6.987 -0.030 0.976 RaceBlack -0.090 0.914 0.621 0.271 3.083 -0.146 0.884 RaceWhite -0.331 0.718 0.599 0.222 2.321 -0.553 0.580 Stage2 0.415 1.515 0.303 0.836 2.745 1.369 0.171 Stage3 1.204 3.333 0.313 1.805 6.155 3.846 0.000 *** Stage4 2.532 12.575 0.389 5.867 26.951 6.509 0.000 *** Purity 0.257 1.293 0.439 0.547 3.056 0.585 0.558 Rsquare = 0.086 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.1e-13 Wald test p = 7.99e-17 Score (logrank) test p = 6.38e-23 SQLE in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.035 9.660000e-01 0.232 0.614 1.521 -0.149 0.881 Age 0.011 1.011000e+00 0.018 0.976 1.046 0.601 0.548 RaceBlack -0.948 3.880000e-01 1.127 0.043 3.530 -0.841 0.400 RaceWhite -1.269 2.810000e-01 1.136 0.030 2.604 -1.117 0.264 Stage2 18.676 1.290812e+08 6488.551 0.000 Inf 0.003 0.998 Stage3 20.095 5.332610e+08 6488.551 0.000 Inf 0.003 0.998 Stage4 21.411 1.988987e+09 6488.551 0.000 Inf 0.003 0.997 Purity 0.764 2.148000e+00 0.956 0.330 14.001 0.799 0.424 Rsquare = 0.157 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.17e-04 Wald test p = 7.09e-03 Score (logrank) test p = 4.59e-06 SQLE in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.295 1.343000e+00 0.389 0.627 2.877 0.759 0.448 Age 0.038 1.039000e+00 0.030 0.980 1.101 1.279 0.201 RaceBlack -2.907 5.500000e-02 1.821 0.002 1.938 -1.597 0.110 RaceWhite -1.577 2.070000e-01 1.456 0.012 3.585 -1.083 0.279 Stage2 17.917 6.042624e+07 15306.153 0.000 Inf 0.001 0.999 Stage3 19.428 2.739588e+08 15306.153 0.000 Inf 0.001 0.999 Stage4 52.478 6.176440e+22 2142676.907 0.000 Inf 0.000 1.000 Purity 3.237 2.544700e+01 2.326 0.266 2430.897 1.391 0.164 Rsquare = 0.376 (max possible = 6.68e-01 ) Likelihood ratio test p = 5.03e-04 Wald test p = 1e+00 Score (logrank) test p = 4.23e-14 SQLE in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.311 1.365 0.144 1.029 1.810 2.157 0.031 * Age 0.050 1.052 0.012 1.028 1.076 4.265 0.000 *** Gendermale -15.419 0.000 3352.212 0.000 Inf -0.005 0.996 RaceBlack -0.637 0.529 1.176 0.053 5.305 -0.541 0.588 RaceWhite -0.086 0.917 1.045 0.118 7.119 -0.082 0.934 Stage2 0.317 1.373 0.374 0.659 2.860 0.847 0.397 Stage3 0.914 2.495 0.393 1.156 5.385 2.329 0.020 * Stage4 2.443 11.509 0.608 3.498 37.869 4.021 0.000 *** Purity -0.066 0.936 0.650 0.262 3.347 -0.102 0.919 Rsquare = 0.079 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.61e-05 Wald test p = 2.76e-06 Score (logrank) test p = 3.65e-08 SQLE in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.032 1.033 0.224 0.666 1.601 0.144 0.886 Age 0.050 1.051 0.021 1.009 1.095 2.405 0.016 * Gendermale 0.981 2.668 1.106 0.305 23.301 0.887 0.375 RaceBlack 16.585 15950211.636 6463.095 0.000 Inf 0.003 0.998 RaceWhite 15.951 8465249.584 6463.095 0.000 Inf 0.002 0.998 Stage2 0.671 1.957 1.075 0.238 16.088 0.624 0.532 Stage3 1.597 4.941 1.060 0.618 39.469 1.507 0.132 Stage4 2.101 8.175 1.174 0.818 81.645 1.789 0.074 · Purity 1.024 2.785 1.324 0.208 37.297 0.774 0.439 Rsquare = 0.105 (max possible = 6.98e-01 ) Likelihood ratio test p = 4.18e-02 Wald test p = 7.23e-02 Score (logrank) test p = 2.46e-02 SQLE in CESC (n=306): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.157 1.170 0.126 0.914 1.499 1.246 0.213 Age 0.010 1.010 0.010 0.990 1.030 0.978 0.328 RaceBlack 0.912 2.490 1.073 0.304 20.406 0.850 0.395 RaceWhite 0.757 2.132 1.016 0.291 15.632 0.745 0.456 Purity 0.571 1.770 0.740 0.415 7.545 0.772 0.440 Rsquare = 0.021 (max possible = 8.91e-01 ) Likelihood ratio test p = 4.52e-01 Wald test p = 4.73e-01 Score (logrank) test p = 4.67e-01 SQLE in CHOL (n=36): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.156 0.856 0.267 0.507 1.444 -0.584 0.559 Age 0.019 1.019 0.023 0.975 1.066 0.849 0.396 Gendermale 0.259 1.296 0.559 0.433 3.875 0.464 0.643 RaceBlack -0.440 0.644 1.509 0.033 12.412 -0.291 0.771 RaceWhite -1.174 0.309 0.919 0.051 1.873 -1.277 0.202 Stage2 0.705 2.023 0.674 0.540 7.577 1.046 0.296 Stage3 -15.887 0.000 6938.379 0.000 Inf -0.002 0.998 Stage4 0.973 2.645 0.719 0.647 10.817 1.354 0.176 Purity 1.774 5.897 1.637 0.238 145.844 1.084 0.278 Rsquare = 0.219 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.48e-01 Wald test p = 6.46e-01 Score (logrank) test p = 4.67e-01 SQLE in COAD (n=458): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.117 0.889 0.145 0.669 1.182 -0.806 0.420 Age 0.023 1.024 0.011 1.001 1.047 2.033 0.042 * Gendermale 0.225 1.252 0.271 0.737 2.128 0.831 0.406 RaceBlack -0.465 0.628 0.831 0.123 3.202 -0.559 0.576 RaceWhite -0.484 0.616 0.778 0.134 2.831 -0.622 0.534 Stage2 0.199 1.221 0.563 0.405 3.677 0.355 0.723 Stage3 0.831 2.297 0.550 0.782 6.743 1.513 0.130 Stage4 1.933 6.908 0.555 2.328 20.499 3.483 0.000 *** Purity -0.263 0.769 0.597 0.239 2.476 -0.441 0.660 Rsquare = 0.111 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.62e-04 Wald test p = 1.18e-04 Score (logrank) test p = 1.72e-05 SQLE in DLBC (n=48): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 1.338 3.813 0.937 0.607 23.939 1.428 0.153 Age -0.008 0.992 0.045 0.907 1.084 -0.183 0.855 Gendermale 0.917 2.503 1.070 0.307 20.391 0.857 0.391 RaceBlack 2.294 9.913 2.323 0.104 940.769 0.987 0.323 RaceWhite -3.071 0.046 1.561 0.002 0.989 -1.967 0.049 * Purity -2.095 0.123 2.023 0.002 6.489 -1.036 0.300 Rsquare = 0.183 (max possible = 5.58e-01 ) Likelihood ratio test p = 2.19e-01 Wald test p = 4.41e-01 Score (logrank) test p = 2.18e-01 SQLE in ESCA (n=185): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.019 1.019 0.146 0.766 1.356 0.129 0.897 Age 0.010 1.010 0.014 0.982 1.038 0.694 0.488 Gendermale 0.489 1.631 0.541 0.565 4.711 0.904 0.366 RaceBlack 0.335 1.398 1.068 0.172 11.340 0.314 0.754 RaceWhite -0.067 0.935 0.457 0.382 2.288 -0.146 0.884 Stage2 0.709 2.032 0.661 0.557 7.418 1.074 0.283 Stage3 1.464 4.324 0.675 1.152 16.232 2.169 0.030 * Stage4 2.872 17.664 0.778 3.847 81.111 3.692 0.000 *** Purity 0.198 1.219 0.779 0.265 5.614 0.254 0.799 Rsquare = 0.141 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.15e-02 Wald test p = 5.32e-03 Score (logrank) test p = 4.4e-04 SQLE in GBM (n=153): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.096 1.100 0.139 0.838 1.445 0.689 0.491 Age 0.030 1.030 0.008 1.014 1.047 3.625 0.000 *** Gendermale -0.084 0.919 0.213 0.605 1.397 -0.394 0.693 RaceBlack 0.530 1.700 0.727 0.409 7.065 0.730 0.466 RaceWhite -0.188 0.828 0.619 0.246 2.786 -0.304 0.761 Purity -1.225 0.294 0.566 0.097 0.890 -2.165 0.030 * Rsquare = 0.132 (max possible = 9.98e-01 ) Likelihood ratio test p = 3.97e-03 Wald test p = 5.33e-03 Score (logrank) test p = 4.65e-03 SQLE in HNSC (n=522): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.233 1.263 0.090 1.057 1.508 2.577 0.010 * Age 0.021 1.022 0.008 1.007 1.037 2.839 0.005 ** Gendermale -0.231 0.793 0.172 0.566 1.112 -1.345 0.179 RaceBlack 0.066 1.069 0.560 0.357 3.201 0.119 0.906 RaceWhite -0.251 0.778 0.511 0.286 2.118 -0.491 0.624 Stage2 0.580 1.787 0.544 0.615 5.192 1.066 0.286 Stage3 0.832 2.299 0.537 0.803 6.581 1.551 0.121 Stage4 1.232 3.426 0.510 1.261 9.313 2.414 0.016 * Purity -0.083 0.920 0.372 0.444 1.908 -0.224 0.823 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.4e-05 Wald test p = 1.27e-04 Score (logrank) test p = 8.53e-05 SQLE in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.986 2.681 0.358 1.330 5.404 2.757 0.006 ** Age 0.006 1.006 0.025 0.959 1.056 0.255 0.799 Gendermale -0.018 0.983 0.538 0.342 2.819 -0.033 0.974 RaceBlack 17.922 60756015.607 13526.743 0.000 Inf 0.001 0.999 RaceWhite 17.671 47243950.228 13526.743 0.000 Inf 0.001 0.999 Stage2 17.471 38671585.978 5658.973 0.000 Inf 0.003 0.998 Stage3 17.253 31107953.779 5658.973 0.000 Inf 0.003 0.998 Stage4 17.493 39538049.487 5658.973 0.000 Inf 0.003 0.998 Purity -1.450 0.235 1.131 0.026 2.155 -1.281 0.200 Rsquare = 0.219 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.57e-02 Wald test p = 2.49e-01 Score (logrank) test p = 1.43e-01 SQLE in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.153 1.165 0.098 0.961 1.412 1.560 0.119 Age 0.027 1.027 0.008 1.010 1.044 3.200 0.001 ** Gendermale -0.278 0.757 0.183 0.529 1.085 -1.517 0.129 RaceBlack -0.056 0.945 0.565 0.312 2.861 -0.099 0.921 RaceWhite -0.401 0.670 0.512 0.245 1.828 -0.782 0.434 Stage2 0.340 1.405 0.554 0.474 4.165 0.614 0.539 Stage3 0.706 2.025 0.541 0.701 5.848 1.304 0.192 Stage4 1.127 3.085 0.512 1.131 8.418 2.200 0.028 * Purity 0.174 1.190 0.406 0.537 2.638 0.428 0.669 Rsquare = 0.091 (max possible = 9.89e-01 ) Likelihood ratio test p = 1.26e-04 Wald test p = 3.89e-04 Score (logrank) test p = 2.92e-04 SQLE in KICH (n=66): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p SQLE 0.977 2.658 0.452 1.095 6.447000e+00 2.162 0.031 Age 0.089 1.093 0.028 1.035 1.154000e+00 3.201 0.001 Gendermale -1.345 0.261 0.728 0.063 1.085000e+00 -1.848 0.065 RaceBlack -15.372 0.000 2557.318 0.000 Inf -0.006 0.995 RaceWhite -1.598 0.202 1.161 0.021 1.969000e+00 -1.376 0.169 Stage2 13.929 1119677.650 0.845 213824.123 5.863127e+06 16.489 0.000 Stage3 15.705 6617793.327 0.792 1402440.959 3.122783e+07 19.839 0.000 Stage4 17.673 47366621.621 0.899 8133380.917 2.758505e+08 19.660 0.000 Purity 1.227 3.411 3.970 0.001 8.169804e+03 0.309 0.757 signif SQLE * Age ** Gendermale · RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.374 (max possible = 6.71e-01 ) Likelihood ratio test p = 5.35e-04 Wald test p = 4.37e-225 Score (logrank) test p = 6.61e-09 SQLE in KIRC (n=533): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.015 1.016 0.149 0.758 1.360 0.103 0.918 Age 0.035 1.036 0.008 1.019 1.053 4.154 0.000 *** Gendermale -0.082 0.921 0.183 0.643 1.320 -0.447 0.655 RaceBlack 0.202 1.224 1.056 0.154 9.704 0.191 0.848 RaceWhite 0.152 1.164 1.014 0.160 8.495 0.150 0.881 Stage2 0.215 1.240 0.344 0.632 2.436 0.626 0.532 Stage3 0.815 2.259 0.233 1.431 3.566 3.499 0.000 *** Stage4 1.759 5.807 0.216 3.805 8.862 8.155 0.000 *** Purity 0.002 1.002 0.372 0.484 2.075 0.005 0.996 Rsquare = 0.174 (max possible = 9.65e-01 ) Likelihood ratio test p = 1.03e-14 Wald test p = 1.07e-14 Score (logrank) test p = 5.78e-18 SQLE in KIRP (n=290): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.588 1.800 0.159 1.317 2.461 3.687 0.000 *** Age 0.027 1.027 0.016 0.996 1.060 1.710 0.087 · Gendermale -0.046 0.955 0.418 0.421 2.165 -0.110 0.912 RaceBlack -2.334 0.097 1.227 0.009 1.073 -1.903 0.057 · RaceWhite -2.423 0.089 1.189 0.009 0.911 -2.038 0.042 * Stage2 -0.272 0.762 1.056 0.096 6.038 -0.258 0.797 Stage3 1.386 4.000 0.428 1.730 9.252 3.241 0.001 ** Stage4 2.405 11.081 0.523 3.976 30.883 4.599 0.000 *** Purity -0.089 0.914 0.725 0.221 3.789 -0.123 0.902 Rsquare = 0.208 (max possible = 7.58e-01 ) Likelihood ratio test p = 6.6e-08 Wald test p = 5.46e-08 Score (logrank) test p = 1.75e-12 SQLE in LAML (n=173): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.289 1.335 0.146 1.003 1.775 1.983 0.047 * Age 0.039 1.040 0.008 1.023 1.057 4.723 0.000 *** Gendermale -0.212 0.809 0.215 0.531 1.235 -0.982 0.326 RaceBlack -0.331 0.718 1.105 0.082 6.266 -0.300 0.764 RaceWhite -0.810 0.445 1.020 0.060 3.282 -0.795 0.427 Rsquare = 0.178 (max possible = 9.96e-01 ) Likelihood ratio test p = 2.15e-05 Wald test p = 1.09e-04 Score (logrank) test p = 7.92e-05 SQLE in LGG (n=516): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.068 1.071 0.133 0.826 1.389 0.516 0.606 Age 0.061 1.063 0.008 1.047 1.079 8.014 0.000 *** Gendermale 0.105 1.111 0.197 0.755 1.634 0.534 0.594 RaceBlack 15.402 4884280.514 1999.968 0.000 Inf 0.008 0.994 RaceWhite 15.422 4986002.655 1999.968 0.000 Inf 0.008 0.994 Purity -0.995 0.370 0.409 0.166 0.824 -2.434 0.015 * Rsquare = 0.137 (max possible = 9.07e-01 ) Likelihood ratio test p = 1.01e-12 Wald test p = 1.01e-12 Score (logrank) test p = 8.07e-14 SQLE in LIHC (n=371): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.128 1.137 0.083 0.966 1.338 1.543 0.123 Age 0.010 1.010 0.008 0.994 1.026 1.223 0.221 Gendermale -0.126 0.881 0.227 0.565 1.375 -0.557 0.578 RaceBlack 0.821 2.272 0.492 0.867 5.955 1.669 0.095 · RaceWhite 0.070 1.072 0.241 0.668 1.720 0.290 0.772 Stage2 0.338 1.402 0.262 0.839 2.342 1.290 0.197 Stage3 0.907 2.476 0.236 1.560 3.930 3.847 0.000 *** Stage4 1.657 5.243 0.618 1.560 17.615 2.680 0.007 ** Purity 0.488 1.629 0.462 0.658 4.029 1.056 0.291 Rsquare = 0.092 (max possible = 9.66e-01 ) Likelihood ratio test p = 4.67e-04 Wald test p = 3.21e-04 Score (logrank) test p = 1.2e-04 SQLE in LUAD (n=515): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.280 1.324 0.092 1.105 1.585 3.049 0.002 ** Age 0.009 1.010 0.009 0.992 1.028 1.046 0.295 Gendermale -0.005 0.995 0.169 0.715 1.385 -0.027 0.978 RaceBlack 16.376 12942081.750 1900.358 0.000 Inf 0.009 0.993 RaceWhite 16.536 15183650.152 1900.358 0.000 Inf 0.009 0.993 Stage2 0.853 2.348 0.202 1.580 3.487 4.227 0.000 *** Stage3 0.976 2.653 0.218 1.730 4.071 4.469 0.000 *** Stage4 1.058 2.880 0.336 1.492 5.559 3.152 0.002 ** Purity 0.523 1.687 0.346 0.856 3.326 1.510 0.131 Rsquare = 0.116 (max possible = 9.74e-01 ) Likelihood ratio test p = 4.52e-08 Wald test p = 8.99e-07 Score (logrank) test p = 8.72e-08 SQLE in LUSC (n=501): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.029 1.029 0.092 0.859 1.233 0.313 0.754 Age 0.016 1.016 0.009 0.998 1.035 1.700 0.089 · Gendermale 0.430 1.537 0.195 1.049 2.250 2.208 0.027 * RaceBlack -0.004 0.996 0.607 0.303 3.275 -0.007 0.995 RaceWhite -0.526 0.591 0.563 0.196 1.784 -0.933 0.351 Stage2 0.202 1.224 0.189 0.845 1.773 1.070 0.285 Stage3 0.595 1.813 0.216 1.187 2.769 2.752 0.006 ** Stage4 0.749 2.115 0.795 0.445 10.047 0.942 0.346 Purity -0.356 0.700 0.367 0.341 1.438 -0.970 0.332 Rsquare = 0.051 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.34e-02 Wald test p = 1.8e-02 Score (logrank) test p = 1.55e-02 SQLE in MESO (n=87): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.675 1.964 0.163 1.426 2.704 4.134 0.000 *** Age 0.017 1.018 0.016 0.987 1.049 1.118 0.264 Gendermale -0.112 0.894 0.330 0.468 1.708 -0.338 0.735 RaceBlack 0.386 1.471 1.518 0.075 28.827 0.254 0.799 RaceWhite -0.511 0.600 1.047 0.077 4.668 -0.488 0.626 Stage2 -0.307 0.736 0.459 0.300 1.808 -0.668 0.504 Stage3 -0.579 0.561 0.439 0.237 1.324 -1.320 0.187 Stage4 -0.581 0.559 0.481 0.218 1.435 -1.208 0.227 Purity -0.626 0.535 0.579 0.172 1.662 -1.082 0.279 Rsquare = 0.26 (max possible = 9.98e-01 ) Likelihood ratio test p = 2.42e-03 Wald test p = 7.98e-03 Score (logrank) test p = 4.74e-03 SQLE in OV (n=303): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.012 0.989 0.091 0.827 1.181 -0.127 0.899 Age 0.036 1.037 0.008 1.020 1.053 4.432 0.000 *** RaceBlack -0.058 0.943 0.578 0.304 2.929 -0.101 0.920 RaceWhite -0.162 0.850 0.517 0.309 2.342 -0.313 0.754 Purity -0.541 0.582 0.670 0.157 2.163 -0.808 0.419 Rsquare = 0.081 (max possible = 9.97e-01 ) Likelihood ratio test p = 1.15e-03 Wald test p = 1e-03 Score (logrank) test p = 8.36e-04 SQLE in PAAD (n=179): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.161 1.175 0.114 0.939 1.470 1.408 0.159 Age 0.022 1.022 0.011 1.001 1.044 2.041 0.041 * Gendermale -0.212 0.809 0.217 0.528 1.239 -0.975 0.330 RaceBlack 0.013 1.013 0.739 0.238 4.313 0.018 0.986 RaceWhite 0.386 1.472 0.476 0.579 3.738 0.812 0.417 Stage2 0.567 1.763 0.438 0.747 4.158 1.295 0.195 Stage3 -0.156 0.856 1.092 0.101 7.280 -0.143 0.886 Stage4 0.029 1.029 0.837 0.200 5.305 0.034 0.973 Purity -0.588 0.555 0.418 0.245 1.259 -1.408 0.159 Rsquare = 0.099 (max possible = 9.91e-01 ) Likelihood ratio test p = 4.47e-02 Wald test p = 7.64e-02 Score (logrank) test p = 7.21e-02 SQLE in PCPG (n=181): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.494 1.638 0.563 0.544 4.937 0.877 0.381 Age 0.041 1.042 0.029 0.985 1.102 1.425 0.154 Gendermale 1.349 3.855 0.885 0.681 21.833 1.525 0.127 RaceBlack -0.535 0.586 19783.806 0.000 Inf 0.000 1.000 RaceWhite 17.093 26499113.109 16230.380 0.000 Inf 0.001 0.999 Purity 5.412 224.120 3.314 0.339 148385.328 1.633 0.102 Rsquare = 0.059 (max possible = 3.07e-01 ) Likelihood ratio test p = 1.27e-01 Wald test p = 3.03e-01 Score (logrank) test p = 2.59e-01 SQLE in PRAD (n=498): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.427 1.533 0.334 0.797 2.950 1.281 0.200 Age 0.013 1.013 0.058 0.904 1.135 0.216 0.829 RaceBlack 16.445 13871204.364 10797.249 0.000 Inf 0.002 0.999 RaceWhite 17.495 39625830.432 10797.249 0.000 Inf 0.002 0.999 Purity 0.909 2.483 1.400 0.160 38.623 0.649 0.516 Rsquare = 0.011 (max possible = 1.83e-01 ) Likelihood ratio test p = 5e-01 Wald test p = 5.96e-01 Score (logrank) test p = 5.23e-01 SQLE in READ (n=166): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.481 1.617 0.537 0.564 4.635 0.895 0.371 Age 0.127 1.135 0.051 1.027 1.254 2.480 0.013 * Gendermale -0.327 0.721 0.711 0.179 2.905 -0.460 0.646 RaceBlack 12.613 300332.700 10265.579 0.000 Inf 0.001 0.999 RaceWhite 11.482 96979.121 10265.579 0.000 Inf 0.001 0.999 Stage2 -1.890 0.151 1.270 0.013 1.821 -1.488 0.137 Stage3 -0.640 0.528 0.957 0.081 3.441 -0.668 0.504 Stage4 -0.171 0.843 0.949 0.131 5.418 -0.180 0.857 Purity 0.248 1.281 1.386 0.085 19.384 0.179 0.858 Rsquare = 0.218 (max possible = 7.22e-01 ) Likelihood ratio test p = 2.83e-02 Wald test p = 2.67e-01 Score (logrank) test p = 4.76e-02 SQLE in SARC (n=260): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.487 1.628 0.103 1.331 1.991 4.742 0.000 *** Age 0.021 1.021 0.008 1.005 1.037 2.593 0.010 * Gendermale 0.073 1.076 0.225 0.692 1.673 0.324 0.746 RaceBlack -0.003 0.997 1.085 0.119 8.364 -0.003 0.998 RaceWhite -0.548 0.578 1.023 0.078 4.294 -0.536 0.592 Purity 1.088 2.967 0.584 0.945 9.317 1.863 0.062 · Rsquare = 0.127 (max possible = 9.75e-01 ) Likelihood ratio test p = 1.81e-05 Wald test p = 2.09e-05 Score (logrank) test p = 1.26e-05 SQLE in SKCM (n=471): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.113 1.120 0.067 0.981 1.278 1.681 0.093 · Age 0.019 1.019 0.005 1.008 1.029 3.552 0.000 *** Gendermale -0.038 0.963 0.157 0.707 1.311 -0.242 0.809 RaceWhite -1.316 0.268 0.402 0.122 0.590 -3.275 0.001 ** Stage2 0.269 1.309 0.218 0.853 2.008 1.232 0.218 Stage3 0.595 1.813 0.204 1.216 2.703 2.917 0.004 ** Stage4 1.404 4.070 0.354 2.035 8.142 3.968 0.000 *** Purity 1.023 2.783 0.341 1.426 5.433 2.999 0.003 ** Rsquare = 0.13 (max possible = 9.92e-01 ) Likelihood ratio test p = 5.96e-09 Wald test p = 4.05e-09 Score (logrank) test p = 4.11e-10 SQLE in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.198 1.219000e+00 0.251 0.745 1.996 0.789 0.430 Age 0.012 1.012000e+00 0.016 0.981 1.045 0.766 0.444 Gendermale 0.265 1.304000e+00 0.441 0.549 3.095 0.601 0.548 RaceWhite -1.189 3.050000e-01 0.633 0.088 1.053 -1.879 0.060 · Stage2 17.460 3.827594e+07 6212.556 0.000 Inf 0.003 0.998 Stage3 17.982 6.447042e+07 6212.556 0.000 Inf 0.003 0.998 Stage4 20.087 5.294554e+08 6212.556 0.000 Inf 0.003 0.997 Purity 0.107 1.113000e+00 0.965 0.168 7.374 0.111 0.912 Rsquare = 0.152 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.98e-02 Wald test p = 4.61e-02 Score (logrank) test p = 3.55e-03 SQLE in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.113 1.119 0.071 0.973 1.287 1.577 0.115 Age 0.021 1.021 0.006 1.010 1.032 3.654 0.000 *** Gendermale -0.046 0.955 0.172 0.681 1.338 -0.267 0.789 RaceWhite -1.124 0.325 0.601 0.100 1.056 -1.869 0.062 · Stage2 0.145 1.156 0.230 0.736 1.815 0.628 0.530 Stage3 0.544 1.722 0.209 1.143 2.594 2.601 0.009 ** Stage4 1.196 3.308 0.402 1.503 7.280 2.972 0.003 ** Purity 1.156 3.178 0.372 1.533 6.587 3.109 0.002 ** Rsquare = 0.141 (max possible = 9.95e-01 ) Likelihood ratio test p = 3.54e-07 Wald test p = 6.73e-07 Score (logrank) test p = 2.29e-07 SQLE in STAD (n=415): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.012 0.989 0.089 0.830 1.177 -0.130 0.897 Age 0.027 1.027 0.010 1.006 1.048 2.573 0.010 * Gendermale 0.126 1.134 0.210 0.752 1.712 0.601 0.548 RaceBlack 0.274 1.315 0.450 0.544 3.178 0.609 0.543 RaceWhite 0.097 1.102 0.245 0.682 1.781 0.397 0.692 Stage2 0.486 1.626 0.390 0.758 3.491 1.248 0.212 Stage3 0.919 2.508 0.363 1.230 5.112 2.530 0.011 * Stage4 1.320 3.744 0.504 1.394 10.055 2.619 0.009 ** Purity -0.549 0.577 0.384 0.272 1.225 -1.430 0.153 Rsquare = 0.069 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.36e-02 Wald test p = 1.87e-02 Score (logrank) test p = 1.52e-02 SQLE in TGCT (n=150): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 227.401 5.739095e+98 77828.793 0 Inf 0.003 0.998 Age 1.932 6.900000e+00 1770.071 0 Inf 0.001 0.999 RaceBlack 135.697 8.556767e+58 325823.955 0 Inf 0.000 1.000 RaceWhite -323.540 0.000000e+00 325818.743 0 Inf -0.001 0.999 Stage2 -65.580 0.000000e+00 47448.744 0 Inf -0.001 0.999 Stage3 -281.313 0.000000e+00 225901.406 0 Inf -0.001 0.999 Purity 184.130 9.263631e+79 254204.152 0 Inf 0.001 0.999 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 1.92e-03 SQLE in THCA (n=509): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.976 2.655 0.384 1.250 5.637 2.542 0.011 * Age 0.152 1.164 0.030 1.098 1.234 5.075 0.000 *** Gendermale -0.131 0.877 0.635 0.253 3.046 -0.206 0.837 RaceBlack 16.201 10860785.567 6401.055 0.000 Inf 0.003 0.998 RaceWhite 16.367 12819818.577 6401.055 0.000 Inf 0.003 0.998 Stage2 0.400 1.492 1.083 0.179 12.461 0.369 0.712 Stage3 0.340 1.405 0.845 0.268 7.356 0.402 0.688 Stage4 2.168 8.737 0.996 1.240 61.537 2.176 0.030 * Purity 2.872 17.666 1.167 1.793 174.069 2.460 0.014 * Rsquare = 0.164 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.19e-11 Wald test p = 1.13e-04 Score (logrank) test p = 1.84e-11 SQLE in THYM (n=120): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE -0.289 0.749 0.352 0.376 1.493 -0.821 0.412 Age 0.056 1.057 0.033 0.991 1.128 1.689 0.091 · Gendermale -0.109 0.897 0.758 0.203 3.963 -0.143 0.886 RaceBlack -16.643 0.000 9820.370 0.000 Inf -0.002 0.999 RaceWhite 0.464 1.590 1.097 0.185 13.643 0.423 0.672 Purity 0.398 1.489 1.102 0.172 12.916 0.361 0.718 Rsquare = 0.05 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.48e-01 Wald test p = 5.87e-01 Score (logrank) test p = 4.69e-01 SQLE in UCEC (n=545): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.345 1.412 0.155 1.041 1.915 2.219 0.027 * Age 0.043 1.044 0.016 1.013 1.077 2.799 0.005 ** RaceBlack -0.574 0.563 0.802 0.117 2.711 -0.716 0.474 RaceWhite -0.537 0.584 0.748 0.135 2.532 -0.718 0.473 Purity 0.296 1.344 0.658 0.370 4.887 0.449 0.653 Rsquare = 0.055 (max possible = 7.81e-01 ) Likelihood ratio test p = 6.64e-03 Wald test p = 5.58e-03 Score (logrank) test p = 5.08e-03 SQLE in UCS (n=57): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.217 1.243 0.266 0.737 2.095 0.816 0.415 Age 0.040 1.040 0.024 0.992 1.091 1.624 0.104 RaceBlack 17.478 38964530.121 6522.703 0.000 Inf 0.003 0.998 RaceWhite 17.667 47056616.868 6522.703 0.000 Inf 0.003 0.998 Purity -0.764 0.466 1.078 0.056 3.853 -0.709 0.479 Rsquare = 0.13 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.03e-01 Wald test p = 2.8e-01 Score (logrank) test p = 1.96e-01 SQLE in UVM (n=80): Model: Surv(OS, EVENT) ~ `SQLE` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SQLE 0.482 1.620 0.180 1.139 2.303 2.687 0.007 ** Age 0.037 1.037 0.018 1.002 1.075 2.048 0.041 * Gendermale 0.683 1.980 0.504 0.738 5.313 1.356 0.175 Stage3 -0.077 0.926 0.510 0.341 2.514 -0.151 0.880 Stage4 3.392 29.722 1.207 2.792 316.454 2.811 0.005 ** Purity 1.156 3.176 1.334 0.233 43.345 0.867 0.386 Rsquare = 0.318 (max possible = 8.72e-01 ) Likelihood ratio test p = 4.91e-05 Wald test p = 5.16e-04 Score (logrank) test p = 1.45e-10 SREBF2 in ACC (n=79): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.210 1.234 0.206 0.824 1.849 1.019 0.308 Age 0.006 1.006 0.014 0.980 1.034 0.450 0.653 Gendermale 0.469 1.598 0.423 0.698 3.659 1.108 0.268 RaceBlack -0.050 0.951 12144.759 0.000 Inf 0.000 1.000 RaceWhite 16.892 21672938.436 10357.084 0.000 Inf 0.002 0.999 Purity 2.276 9.738 2.409 0.087 1092.992 0.945 0.345 Rsquare = 0.082 (max possible = 9.38e-01 ) Likelihood ratio test p = 4.85e-01 Wald test p = 7.63e-01 Score (logrank) test p = 5.98e-01 SREBF2 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.310 1.364 0.129 1.058 1.758 2.398 0.016 * Age 0.031 1.032 0.009 1.015 1.049 3.681 0.000 *** Gendermale -0.148 0.862 0.179 0.607 1.225 -0.828 0.408 RaceBlack 0.721 2.057 0.447 0.856 4.941 1.613 0.107 RaceWhite 0.191 1.211 0.356 0.603 2.431 0.537 0.591 Stage2 14.603 2198650.851 1871.857 0.000 Inf 0.008 0.994 Stage3 15.107 3637213.719 1871.857 0.000 Inf 0.008 0.994 Stage4 15.597 5936804.192 1871.857 0.000 Inf 0.008 0.993 Purity 0.006 1.006 0.341 0.516 1.961 0.017 0.987 Rsquare = 0.145 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.64e-08 Wald test p = 6.99e-08 Score (logrank) test p = 1.96e-08 SREBF2 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.076 0.927 0.141 0.704 1.221 -0.539 0.590 Age 0.035 1.036 0.008 1.020 1.051 4.540 0.000 *** Gendermale 0.051 1.052 1.007 0.146 7.579 0.051 0.960 RaceBlack 0.008 1.008 0.619 0.299 3.391 0.012 0.990 RaceWhite -0.213 0.808 0.596 0.251 2.598 -0.358 0.720 Stage2 0.392 1.480 0.305 0.814 2.691 1.285 0.199 Stage3 1.173 3.231 0.314 1.746 5.977 3.736 0.000 *** Stage4 2.533 12.594 0.390 5.861 27.061 6.491 0.000 *** Purity 0.539 1.715 0.423 0.749 3.927 1.276 0.202 Rsquare = 0.081 (max possible = 7.85e-01 ) Likelihood ratio test p = 2.18e-12 Wald test p = 4.7e-16 Score (logrank) test p = 6.22e-22 SREBF2 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.454 6.350000e-01 0.344 0.324 1.246 -1.320 0.187 Age 0.009 1.009000e+00 0.017 0.976 1.044 0.552 0.581 RaceBlack -1.183 3.060000e-01 1.127 0.034 2.790 -1.050 0.294 RaceWhite -1.529 2.170000e-01 1.138 0.023 2.016 -1.344 0.179 Stage2 18.545 1.132087e+08 6521.941 0.000 Inf 0.003 0.998 Stage3 19.882 4.313079e+08 6521.941 0.000 Inf 0.003 0.998 Stage4 21.200 1.610884e+09 6521.941 0.000 Inf 0.003 0.997 Purity 0.821 2.274000e+00 0.930 0.367 14.083 0.883 0.377 Rsquare = 0.166 (max possible = 7.18e-01 ) Likelihood ratio test p = 2.53e-04 Wald test p = 4.82e-03 Score (logrank) test p = 2.24e-06 SREBF2 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.736 2.087000e+00 0.754 0.476 9.148 0.976 0.329 Age 0.037 1.038000e+00 0.028 0.983 1.096 1.331 0.183 RaceBlack -2.456 8.600000e-02 1.915 0.002 3.657 -1.283 0.200 RaceWhite -1.358 2.570000e-01 1.499 0.014 4.857 -0.906 0.365 Stage2 17.944 6.210566e+07 15132.732 0.000 Inf 0.001 0.999 Stage3 19.423 2.724947e+08 15132.732 0.000 Inf 0.001 0.999 Stage4 52.032 3.955540e+22 2338917.598 0.000 Inf 0.000 1.000 Purity 2.134 8.450000e+00 2.319 0.090 796.076 0.920 0.357 Rsquare = 0.381 (max possible = 6.68e-01 ) Likelihood ratio test p = 4.23e-04 Wald test p = 1e+00 Score (logrank) test p = 3.18e-14 SREBF2 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.141 0.869 0.224 0.560 1.348 -0.627 0.530 Age 0.048 1.049 0.012 1.025 1.074 3.995 0.000 *** Gendermale -15.375 0.000 3455.605 0.000 Inf -0.004 0.996 RaceBlack -0.428 0.652 1.175 0.065 6.529 -0.364 0.716 RaceWhite 0.284 1.329 1.034 0.175 10.081 0.275 0.783 Stage2 0.288 1.334 0.379 0.634 2.805 0.760 0.447 Stage3 0.808 2.242 0.402 1.019 4.933 2.007 0.045 * Stage4 2.189 8.930 0.595 2.781 28.673 3.679 0.000 *** Purity 0.330 1.391 0.611 0.420 4.610 0.540 0.589 Rsquare = 0.071 (max possible = 6.81e-01 ) Likelihood ratio test p = 8.88e-05 Wald test p = 1.65e-05 Score (logrank) test p = 3.24e-07 SREBF2 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.308 0.735 0.318 0.394 1.371 -0.968 0.333 Age 0.043 1.044 0.022 1.000 1.089 1.974 0.048 * Gendermale 1.065 2.900 1.106 0.332 25.330 0.963 0.336 RaceBlack 16.640 16859272.117 6881.928 0.000 Inf 0.002 0.998 RaceWhite 15.941 8380168.998 6881.928 0.000 Inf 0.002 0.998 Stage2 0.684 1.982 1.072 0.243 16.188 0.638 0.523 Stage3 1.715 5.559 1.069 0.684 45.156 1.605 0.108 Stage4 2.272 9.703 1.199 0.926 101.658 1.896 0.058 · Purity 1.049 2.855 1.307 0.220 37.000 0.803 0.422 Rsquare = 0.111 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.08e-02 Wald test p = 4.61e-02 Score (logrank) test p = 1.42e-02 SREBF2 in CESC (n=306): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.115 0.891 0.203 0.599 1.326 -0.568 0.570 Age 0.012 1.012 0.010 0.992 1.032 1.189 0.235 RaceBlack 1.085 2.959 1.070 0.363 24.111 1.014 0.311 RaceWhite 0.845 2.329 1.016 0.318 17.049 0.832 0.405 Purity 0.577 1.780 0.735 0.421 7.524 0.784 0.433 Rsquare = 0.015 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.31e-01 Wald test p = 6.71e-01 Score (logrank) test p = 6.63e-01 SREBF2 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.202 0.817 0.587 0.259 2.581 -0.344 0.731 Age 0.018 1.018 0.022 0.976 1.063 0.828 0.408 Gendermale 0.316 1.372 0.595 0.427 4.408 0.531 0.595 RaceBlack -0.117 0.889 1.636 0.036 21.943 -0.072 0.943 RaceWhite -1.025 0.359 0.906 0.061 2.120 -1.131 0.258 Stage2 0.776 2.172 0.750 0.499 9.449 1.034 0.301 Stage3 -15.570 0.000 6943.339 0.000 Inf -0.002 0.998 Stage4 1.114 3.047 1.071 0.374 24.842 1.041 0.298 Purity 1.846 6.334 1.643 0.253 158.458 1.124 0.261 Rsquare = 0.214 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.69e-01 Wald test p = 6.52e-01 Score (logrank) test p = 4.82e-01 SREBF2 in COAD (n=458): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.154 1.167 0.219 0.760 1.792 0.706 0.480 Age 0.025 1.025 0.012 1.002 1.048 2.123 0.034 * Gendermale 0.228 1.256 0.269 0.741 2.129 0.847 0.397 RaceBlack -0.340 0.712 0.835 0.138 3.658 -0.407 0.684 RaceWhite -0.385 0.680 0.779 0.148 3.135 -0.494 0.621 Stage2 0.233 1.262 0.563 0.419 3.805 0.413 0.679 Stage3 0.824 2.280 0.551 0.775 6.712 1.497 0.135 Stage4 1.880 6.553 0.553 2.215 19.388 3.397 0.001 ** Purity -0.212 0.809 0.605 0.247 2.646 -0.351 0.726 Rsquare = 0.111 (max possible = 9.04e-01 ) Likelihood ratio test p = 3.8e-04 Wald test p = 1.4e-04 Score (logrank) test p = 1.99e-05 SREBF2 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.529 1.698 1.304 0.132 21.875 0.406 0.685 Age 0.000 1.000 0.043 0.919 1.088 -0.005 0.996 Gendermale 0.678 1.970 1.065 0.244 15.882 0.637 0.524 RaceBlack 0.811 2.249 1.914 0.053 95.695 0.424 0.672 RaceWhite -2.194 0.112 1.290 0.009 1.398 -1.700 0.089 · Purity -1.711 0.181 2.153 0.003 12.286 -0.795 0.427 Rsquare = 0.134 (max possible = 5.58e-01 ) Likelihood ratio test p = 4.34e-01 Wald test p = 5.64e-01 Score (logrank) test p = 3.12e-01 SREBF2 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.096 0.908 0.250 0.556 1.484 -0.385 0.701 Age 0.009 1.009 0.014 0.982 1.037 0.639 0.523 Gendermale 0.457 1.579 0.541 0.547 4.558 0.844 0.399 RaceBlack 0.398 1.488 1.081 0.179 12.376 0.368 0.713 RaceWhite -0.077 0.926 0.446 0.386 2.220 -0.173 0.862 Stage2 0.694 2.002 0.655 0.555 7.222 1.061 0.289 Stage3 1.448 4.253 0.672 1.140 15.860 2.155 0.031 * Stage4 2.850 17.285 0.776 3.776 79.117 3.672 0.000 *** Purity 0.311 1.365 0.809 0.280 6.658 0.385 0.700 Rsquare = 0.142 (max possible = 9.32e-01 ) Likelihood ratio test p = 1.09e-02 Wald test p = 5.18e-03 Score (logrank) test p = 4.27e-04 SREBF2 in GBM (n=153): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.148 0.862 0.137 0.659 1.128 -1.079 0.280 Age 0.030 1.030 0.008 1.013 1.047 3.559 0.000 *** Gendermale -0.109 0.897 0.214 0.590 1.363 -0.510 0.610 RaceBlack 0.502 1.652 0.726 0.398 6.862 0.691 0.490 RaceWhite -0.308 0.735 0.617 0.219 2.466 -0.498 0.618 Purity -0.925 0.397 0.562 0.132 1.192 -1.647 0.100 Rsquare = 0.136 (max possible = 9.98e-01 ) Likelihood ratio test p = 3.02e-03 Wald test p = 4.98e-03 Score (logrank) test p = 4.33e-03 SREBF2 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.003 1.003 0.126 0.784 1.284 0.023 0.981 Age 0.022 1.022 0.008 1.007 1.038 2.898 0.004 ** Gendermale -0.249 0.779 0.172 0.556 1.092 -1.447 0.148 RaceBlack 0.133 1.142 0.560 0.381 3.424 0.238 0.812 RaceWhite -0.248 0.781 0.512 0.286 2.129 -0.484 0.629 Stage2 0.617 1.853 0.544 0.638 5.379 1.134 0.257 Stage3 0.851 2.343 0.537 0.817 6.718 1.584 0.113 Stage4 1.255 3.507 0.510 1.291 9.530 2.460 0.014 * Purity -0.047 0.954 0.366 0.465 1.955 -0.129 0.897 Rsquare = 0.069 (max possible = 9.89e-01 ) Likelihood ratio test p = 5.27e-04 Wald test p = 1.39e-03 Score (logrank) test p = 1.02e-03 SREBF2 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.444 6.420000e-01 0.445 0.268 1.536 -0.996 0.319 Age 0.008 1.008000e+00 0.026 0.959 1.060 0.309 0.757 Gendermale -0.150 8.600000e-01 0.548 0.294 2.519 -0.274 0.784 RaceBlack 18.822 1.493863e+08 12224.013 0.000 Inf 0.002 0.999 RaceWhite 18.009 6.626519e+07 12224.013 0.000 Inf 0.001 0.999 Stage2 17.278 3.191039e+07 5388.713 0.000 Inf 0.003 0.997 Stage3 16.327 1.232728e+07 5388.713 0.000 Inf 0.003 0.998 Stage4 17.208 2.974831e+07 5388.713 0.000 Inf 0.003 0.997 Purity -1.427 2.400000e-01 1.050 0.031 1.881 -1.358 0.174 Rsquare = 0.101 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.48e-01 Wald test p = 8.91e-01 Score (logrank) test p = 7.69e-01 SREBF2 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.030 1.030 0.135 0.790 1.344 0.221 0.825 Age 0.027 1.027 0.008 1.010 1.044 3.151 0.002 ** Gendermale -0.288 0.749 0.183 0.523 1.073 -1.574 0.116 RaceBlack -0.028 0.972 0.566 0.321 2.948 -0.049 0.961 RaceWhite -0.404 0.668 0.513 0.244 1.826 -0.787 0.431 Stage2 0.368 1.445 0.554 0.488 4.278 0.665 0.506 Stage3 0.733 2.081 0.541 0.720 6.014 1.353 0.176 Stage4 1.148 3.153 0.512 1.156 8.600 2.243 0.025 * Purity 0.200 1.222 0.403 0.555 2.690 0.497 0.619 Rsquare = 0.085 (max possible = 9.89e-01 ) Likelihood ratio test p = 3.34e-04 Wald test p = 9.3e-04 Score (logrank) test p = 7.05e-04 SREBF2 in KICH (n=66): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p SREBF2 0.545 1.724000e+00 0.548 0.588 5.052000e+00 0.993 0.321 Age 0.082 1.086000e+00 0.029 1.026 1.149000e+00 2.842 0.004 Gendermale -0.853 4.260000e-01 0.729 0.102 1.778000e+00 -1.171 0.242 RaceBlack -16.595 0.000000e+00 6246.273 0.000 Inf -0.003 0.998 RaceWhite -1.473 2.290000e-01 1.161 0.024 2.232000e+00 -1.268 0.205 Stage2 15.901 8.047428e+06 0.850 1521866.797 4.255373e+07 18.713 0.000 Stage3 17.164 2.846451e+07 0.777 6210290.075 1.304655e+08 22.097 0.000 Stage4 19.701 3.597241e+08 0.903 61335296.383 2.109739e+09 21.828 0.000 Purity -0.450 6.380000e-01 3.536 0.001 6.527330e+02 -0.127 0.899 signif SREBF2 Age ** Gendermale RaceBlack RaceWhite Stage2 *** Stage3 *** Stage4 *** Purity Rsquare = 0.35 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.35e-03 Wald test p = 4.65e-280 Score (logrank) test p = 1.03e-08 SREBF2 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.103 1.109 0.186 0.770 1.596 0.554 0.580 Age 0.036 1.036 0.009 1.019 1.054 4.182 0.000 *** Gendermale -0.065 0.937 0.186 0.651 1.350 -0.347 0.728 RaceBlack 0.157 1.170 1.060 0.147 9.334 0.148 0.882 RaceWhite 0.121 1.128 1.016 0.154 8.259 0.119 0.905 Stage2 0.222 1.248 0.345 0.635 2.453 0.644 0.520 Stage3 0.834 2.304 0.234 1.457 3.642 3.570 0.000 *** Stage4 1.781 5.938 0.220 3.862 9.131 8.116 0.000 *** Purity -0.019 0.981 0.369 0.476 2.022 -0.052 0.959 Rsquare = 0.174 (max possible = 9.65e-01 ) Likelihood ratio test p = 9e-15 Wald test p = 1.03e-14 Score (logrank) test p = 5.32e-18 SREBF2 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.365 0.694 0.393 0.321 1.500 -0.929 0.353 Age 0.004 1.004 0.016 0.974 1.036 0.281 0.779 Gendermale -0.546 0.579 0.389 0.270 1.242 -1.402 0.161 RaceBlack -1.641 0.194 1.255 0.017 2.268 -1.308 0.191 RaceWhite -1.688 0.185 1.244 0.016 2.116 -1.357 0.175 Stage2 -0.425 0.654 1.056 0.083 5.173 -0.403 0.687 Stage3 1.620 5.052 0.426 2.191 11.649 3.800 0.000 *** Stage4 2.780 16.120 0.513 5.902 44.029 5.423 0.000 *** Purity -0.264 0.768 0.750 0.176 3.342 -0.352 0.725 Rsquare = 0.166 (max possible = 7.58e-01 ) Likelihood ratio test p = 8.14e-06 Wald test p = 2.12e-06 Score (logrank) test p = 4.11e-10 SREBF2 in LAML (n=173): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.569 1.767 0.199 1.196 2.610 2.858 0.004 ** Age 0.038 1.039 0.008 1.022 1.056 4.659 0.000 *** Gendermale -0.229 0.795 0.215 0.522 1.212 -1.066 0.286 RaceBlack -0.442 0.643 1.106 0.074 5.612 -0.400 0.689 RaceWhite -0.908 0.403 1.022 0.054 2.986 -0.889 0.374 Rsquare = 0.204 (max possible = 9.96e-01 ) Likelihood ratio test p = 2.3e-06 Wald test p = 1.25e-05 Score (logrank) test p = 6.55e-06 SREBF2 in LGG (n=516): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.617 0.540 0.152 0.401 0.726 -4.069 0.000 *** Age 0.061 1.063 0.008 1.047 1.079 7.825 0.000 *** Gendermale -0.074 0.928 0.198 0.629 1.369 -0.375 0.708 RaceBlack 15.341 4596150.604 2127.960 0.000 Inf 0.007 0.994 RaceWhite 15.448 5114388.610 2127.960 0.000 Inf 0.007 0.994 Purity -0.682 0.505 0.427 0.219 1.166 -1.600 0.110 Rsquare = 0.165 (max possible = 9.07e-01 ) Likelihood ratio test p = 6.24e-16 Wald test p = 5.32e-16 Score (logrank) test p = 7.25e-18 SREBF2 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.014 0.986 0.130 0.765 1.271 -0.108 0.914 Age 0.011 1.011 0.008 0.995 1.027 1.345 0.179 Gendermale -0.144 0.866 0.229 0.552 1.356 -0.630 0.529 RaceBlack 0.893 2.444 0.489 0.937 6.370 1.828 0.068 · RaceWhite -0.001 0.999 0.239 0.626 1.595 -0.003 0.998 Stage2 0.312 1.367 0.261 0.819 2.280 1.196 0.232 Stage3 0.952 2.591 0.236 1.632 4.112 4.038 0.000 *** Stage4 1.596 4.935 0.620 1.463 16.641 2.574 0.010 * Purity 0.588 1.800 0.467 0.721 4.493 1.260 0.208 Rsquare = 0.085 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.2e-03 Wald test p = 7.25e-04 Score (logrank) test p = 2.73e-04 SREBF2 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.020 1.020 0.125 0.799 1.303 0.160 0.873 Age 0.007 1.007 0.009 0.989 1.025 0.762 0.446 Gendermale 0.018 1.018 0.169 0.731 1.417 0.105 0.917 RaceBlack 16.088 9700055.757 1882.968 0.000 Inf 0.009 0.993 RaceWhite 16.267 11608723.126 1882.968 0.000 Inf 0.009 0.993 Stage2 0.865 2.375 0.201 1.602 3.521 4.305 0.000 *** Stage3 1.012 2.751 0.218 1.794 4.219 4.637 0.000 *** Stage4 1.008 2.739 0.334 1.423 5.275 3.015 0.003 ** Purity 0.586 1.797 0.345 0.913 3.536 1.698 0.090 · Rsquare = 0.097 (max possible = 9.74e-01 ) Likelihood ratio test p = 2.37e-06 Wald test p = 3.06e-05 Score (logrank) test p = 3.58e-06 SREBF2 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.027 1.027 0.111 0.827 1.276 0.243 0.808 Age 0.016 1.016 0.009 0.998 1.035 1.703 0.089 · Gendermale 0.432 1.541 0.194 1.053 2.254 2.228 0.026 * RaceBlack 0.005 1.005 0.607 0.306 3.300 0.007 0.994 RaceWhite -0.523 0.593 0.564 0.196 1.790 -0.928 0.354 Stage2 0.210 1.234 0.187 0.855 1.779 1.124 0.261 Stage3 0.605 1.831 0.214 1.203 2.786 2.822 0.005 ** Stage4 0.760 2.139 0.795 0.450 10.167 0.956 0.339 Purity -0.363 0.696 0.372 0.335 1.443 -0.974 0.330 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.38e-02 Wald test p = 1.82e-02 Score (logrank) test p = 1.57e-02 SREBF2 in MESO (n=87): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.364 1.439 0.190 0.992 2.087 1.919 0.055 · Age 0.015 1.015 0.016 0.983 1.047 0.911 0.362 Gendermale -0.144 0.866 0.333 0.451 1.662 -0.434 0.665 RaceBlack -0.032 0.969 1.530 0.048 19.439 -0.021 0.983 RaceWhite -0.667 0.513 1.050 0.066 4.023 -0.635 0.526 Stage2 -0.295 0.744 0.465 0.299 1.853 -0.635 0.526 Stage3 -0.216 0.806 0.423 0.352 1.846 -0.511 0.609 Stage4 -0.214 0.808 0.474 0.319 2.044 -0.451 0.652 Purity -0.601 0.548 0.556 0.184 1.631 -1.081 0.280 Rsquare = 0.101 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.29e-01 Wald test p = 4.4e-01 Score (logrank) test p = 4.19e-01 SREBF2 in OV (n=303): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.118 1.125 0.105 0.916 1.382 1.121 0.262 Age 0.036 1.037 0.008 1.021 1.054 4.451 0.000 *** RaceBlack -0.004 0.996 0.578 0.321 3.094 -0.007 0.994 RaceWhite -0.122 0.885 0.517 0.321 2.436 -0.237 0.813 Purity -0.542 0.582 0.671 0.156 2.165 -0.808 0.419 Rsquare = 0.086 (max possible = 9.97e-01 ) Likelihood ratio test p = 6.74e-04 Wald test p = 6.35e-04 Score (logrank) test p = 5.19e-04 SREBF2 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.167 0.846 0.170 0.607 1.181 -0.981 0.326 Age 0.023 1.024 0.011 1.002 1.046 2.119 0.034 * Gendermale -0.217 0.805 0.216 0.527 1.229 -1.006 0.315 RaceBlack -0.018 0.982 0.738 0.231 4.167 -0.025 0.980 RaceWhite 0.298 1.347 0.477 0.528 3.434 0.624 0.533 Stage2 0.590 1.804 0.439 0.762 4.268 1.342 0.179 Stage3 -0.404 0.668 1.106 0.076 5.832 -0.365 0.715 Stage4 0.222 1.248 0.824 0.248 6.279 0.269 0.788 Purity -0.697 0.498 0.408 0.224 1.108 -1.709 0.087 · Rsquare = 0.094 (max possible = 9.91e-01 ) Likelihood ratio test p = 6.23e-02 Wald test p = 8.6e-02 Score (logrank) test p = 7.95e-02 SREBF2 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.932 0.394 0.574 0.128 1.213 -1.624 0.104 Age 0.038 1.039 0.029 0.981 1.100 1.295 0.195 Gendermale 1.653 5.221 0.979 0.766 35.594 1.687 0.092 · RaceBlack 0.310 1.363 21127.331 0.000 Inf 0.000 1.000 RaceWhite 17.589 43515073.718 16722.058 0.000 Inf 0.001 0.999 Purity 4.736 114.034 3.247 0.196 66230.374 1.459 0.145 Rsquare = 0.069 (max possible = 3.07e-01 ) Likelihood ratio test p = 6.67e-02 Wald test p = 1.72e-01 Score (logrank) test p = 1.3e-01 SREBF2 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.402 1.495 0.651 0.417 5.357 0.617 0.537 Age 0.011 1.011 0.057 0.905 1.130 0.198 0.843 RaceBlack 15.230 4115783.532 6808.834 0.000 Inf 0.002 0.998 RaceWhite 16.335 12417713.455 6808.834 0.000 Inf 0.002 0.998 Purity 1.004 2.728 1.404 0.174 42.721 0.715 0.475 Rsquare = 0.008 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.85e-01 Wald test p = 8.05e-01 Score (logrank) test p = 7.42e-01 SREBF2 in READ (n=166): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 1.203 3.331 0.731 0.794 13.970 1.645 0.100 Age 0.142 1.153 0.051 1.042 1.275 2.766 0.006 ** Gendermale -0.371 0.690 0.677 0.183 2.600 -0.548 0.583 RaceBlack 13.504 731989.560 10734.273 0.000 Inf 0.001 0.999 RaceWhite 12.071 174721.243 10734.273 0.000 Inf 0.001 0.999 Stage2 -2.131 0.119 1.274 0.010 1.443 -1.672 0.094 · Stage3 -0.741 0.477 0.952 0.074 3.078 -0.778 0.436 Stage4 -0.595 0.551 1.026 0.074 4.121 -0.580 0.562 Purity 0.118 1.125 1.226 0.102 12.427 0.096 0.923 Rsquare = 0.239 (max possible = 7.22e-01 ) Likelihood ratio test p = 1.4e-02 Wald test p = 1.64e-01 Score (logrank) test p = 3.56e-02 SREBF2 in SARC (n=260): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.132 1.141 0.127 0.890 1.462 1.040 0.298 Age 0.023 1.023 0.008 1.006 1.040 2.732 0.006 ** Gendermale -0.001 0.999 0.223 0.645 1.545 -0.007 0.995 RaceBlack -0.124 0.884 1.086 0.105 7.424 -0.114 0.909 RaceWhite -0.463 0.629 1.023 0.085 4.671 -0.453 0.651 Purity 0.813 2.254 0.586 0.715 7.109 1.387 0.165 Rsquare = 0.047 (max possible = 9.75e-01 ) Likelihood ratio test p = 8.2e-02 Wald test p = 1.12e-01 Score (logrank) test p = 1.1e-01 SREBF2 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.110 1.116 0.097 0.922 1.350 1.127 0.260 Age 0.018 1.018 0.005 1.008 1.029 3.496 0.000 *** Gendermale -0.057 0.945 0.157 0.694 1.286 -0.359 0.719 RaceWhite -1.320 0.267 0.403 0.121 0.588 -3.278 0.001 ** Stage2 0.265 1.303 0.218 0.850 1.998 1.216 0.224 Stage3 0.605 1.831 0.204 1.228 2.730 2.970 0.003 ** Stage4 1.393 4.029 0.354 2.013 8.065 3.935 0.000 *** Purity 0.964 2.621 0.343 1.338 5.135 2.809 0.005 ** Rsquare = 0.126 (max possible = 9.92e-01 ) Likelihood ratio test p = 1.27e-08 Wald test p = 6.92e-09 Score (logrank) test p = 7.52e-10 SREBF2 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.309 7.340000e-01 0.388 0.343 1.571 -0.796 0.426 Age 0.012 1.012000e+00 0.016 0.981 1.044 0.779 0.436 Gendermale 0.102 1.108000e+00 0.449 0.459 2.673 0.228 0.820 RaceWhite -1.228 2.930000e-01 0.611 0.088 0.970 -2.009 0.045 * Stage2 17.486 3.926244e+07 6157.895 0.000 Inf 0.003 0.998 Stage3 18.040 6.832840e+07 6157.895 0.000 Inf 0.003 0.998 Stage4 20.092 5.318149e+08 6157.895 0.000 Inf 0.003 0.997 Purity 0.572 1.772000e+00 1.007 0.246 12.745 0.568 0.570 Rsquare = 0.152 (max possible = 8.69e-01 ) Likelihood ratio test p = 4.96e-02 Wald test p = 5e-02 Score (logrank) test p = 3.92e-03 SREBF2 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.152 1.165 0.103 0.952 1.424 1.483 0.138 Age 0.020 1.020 0.006 1.009 1.032 3.586 0.000 *** Gendermale -0.072 0.930 0.172 0.664 1.304 -0.419 0.675 RaceWhite -1.122 0.326 0.602 0.100 1.059 -1.864 0.062 · Stage2 0.134 1.143 0.230 0.729 1.794 0.583 0.560 Stage3 0.554 1.741 0.208 1.157 2.619 2.660 0.008 ** Stage4 1.201 3.322 0.403 1.508 7.317 2.980 0.003 ** Purity 1.069 2.913 0.373 1.403 6.046 2.869 0.004 ** Rsquare = 0.14 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.27e-07 Wald test p = 6.51e-07 Score (logrank) test p = 2.22e-07 SREBF2 in STAD (n=415): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.282 0.754 0.145 0.567 1.002 -1.945 0.052 · Age 0.028 1.029 0.010 1.008 1.050 2.775 0.006 ** Gendermale 0.163 1.177 0.208 0.782 1.771 0.782 0.434 RaceBlack 0.320 1.377 0.448 0.572 3.315 0.713 0.476 RaceWhite 0.144 1.154 0.246 0.713 1.868 0.585 0.559 Stage2 0.485 1.623 0.390 0.756 3.486 1.243 0.214 Stage3 0.951 2.588 0.364 1.268 5.281 2.612 0.009 ** Stage4 1.353 3.868 0.506 1.434 10.431 2.672 0.008 ** Purity -0.432 0.650 0.386 0.305 1.383 -1.119 0.263 Rsquare = 0.081 (max possible = 9.79e-01 ) Likelihood ratio test p = 3.51e-03 Wald test p = 4.83e-03 Score (logrank) test p = 3.7e-03 SREBF2 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -30.022 0.000000e+00 51520.841 0 Inf -0.001 1.000 Age -3.815 2.200000e-02 2958.163 0 Inf -0.001 0.999 RaceBlack -9.624 0.000000e+00 11857736.540 0 Inf 0.000 1.000 RaceWhite -94.043 0.000000e+00 11873557.940 0 Inf 0.000 1.000 Stage2 -7.357 1.000000e-03 71989.142 0 Inf 0.000 1.000 Stage3 56.332 2.916469e+24 413701.408 0 Inf 0.000 1.000 Purity 108.287 1.067375e+47 406905.828 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 9.29e-04 SREBF2 in THCA (n=509): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.817 2.265 0.582 0.724 7.085 1.405 0.160 Age 0.153 1.165 0.029 1.099 1.234 5.176 0.000 *** Gendermale -0.020 0.980 0.635 0.282 3.406 -0.031 0.975 RaceBlack 17.907 59825947.589 8990.376 0.000 Inf 0.002 0.998 RaceWhite 17.703 48798956.739 8990.376 0.000 Inf 0.002 0.998 Stage2 0.485 1.624 1.197 0.155 16.961 0.405 0.686 Stage3 0.470 1.599 0.891 0.279 9.174 0.527 0.598 Stage4 2.091 8.093 1.047 1.039 63.021 1.997 0.046 * Purity 2.316 10.130 1.082 1.216 84.420 2.140 0.032 * Rsquare = 0.153 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.05e-10 Wald test p = 3.27e-04 Score (logrank) test p = 8.49e-11 SREBF2 in THYM (n=120): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.964 0.381 0.797 0.080 1.818 -1.210 0.226 Age 0.042 1.042 0.031 0.981 1.108 1.331 0.183 Gendermale -0.189 0.828 0.770 0.183 3.747 -0.245 0.806 RaceBlack -16.302 0.000 10313.632 0.000 Inf -0.002 0.999 RaceWhite 0.529 1.697 1.122 0.188 15.294 0.472 0.637 Purity 0.314 1.369 1.138 0.147 12.735 0.276 0.782 Rsquare = 0.057 (max possible = 4.51e-01 ) Likelihood ratio test p = 3.54e-01 Wald test p = 4.55e-01 Score (logrank) test p = 3.43e-01 SREBF2 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.018 1.018 0.264 0.607 1.708 0.067 0.946 Age 0.050 1.051 0.016 1.019 1.084 3.163 0.002 ** RaceBlack -0.409 0.664 0.797 0.139 3.169 -0.513 0.608 RaceWhite -0.524 0.592 0.745 0.137 2.553 -0.703 0.482 Purity 0.447 1.563 0.646 0.441 5.539 0.692 0.489 Rsquare = 0.038 (max possible = 7.81e-01 ) Likelihood ratio test p = 5.02e-02 Wald test p = 5.93e-02 Score (logrank) test p = 5.77e-02 SREBF2 in UCS (n=57): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 -0.271 0.763 0.334 0.396 1.469 -0.810 0.418 Age 0.042 1.043 0.024 0.996 1.093 1.784 0.074 · RaceBlack 17.473 38768673.980 6530.841 0.000 Inf 0.003 0.998 RaceWhite 17.709 49073680.078 6530.841 0.000 Inf 0.003 0.998 Purity -0.907 0.404 1.060 0.051 3.222 -0.856 0.392 Rsquare = 0.13 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.04e-01 Wald test p = 2.84e-01 Score (logrank) test p = 2.05e-01 SREBF2 in UVM (n=80): Model: Surv(OS, EVENT) ~ `SREBF2` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif SREBF2 0.334 1.397 0.400 0.638 3.061 0.835 0.404 Age 0.041 1.042 0.019 1.004 1.082 2.149 0.032 * Gendermale 0.349 1.418 0.496 0.536 3.753 0.704 0.482 Stage3 0.176 1.192 0.526 0.425 3.344 0.334 0.738 Stage4 3.701 40.502 1.217 3.726 440.240 3.040 0.002 ** Purity 1.930 6.892 1.240 0.607 78.248 1.557 0.119 Rsquare = 0.259 (max possible = 8.72e-01 ) Likelihood ratio test p = 7.57e-04 Wald test p = 2.78e-03 Score (logrank) test p = 2.02e-09 TM7SF2 in ACC (n=79): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 64 patients with 24 dying ( 15 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.181 0.834 0.106 0.678 1.026 -1.715 0.086 · Age 0.008 1.008 0.014 0.981 1.036 0.588 0.557 Gendermale 0.242 1.273 0.429 0.550 2.951 0.564 0.573 RaceBlack 0.492 1.636 12314.123 0.000 Inf 0.000 1.000 RaceWhite 16.629 16662171.357 10484.169 0.000 Inf 0.002 0.999 Purity 3.365 28.942 2.346 0.291 2875.685 1.434 0.151 Rsquare = 0.107 (max possible = 9.38e-01 ) Likelihood ratio test p = 3.02e-01 Wald test p = 5.62e-01 Score (logrank) test p = 4.07e-01 TM7SF2 in BLCA (n=408): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 348 patients with 160 dying ( 60 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.016 1.017 0.059 0.906 1.140 0.281 0.778 Age 0.033 1.034 0.009 1.016 1.051 3.817 0.000 *** Gendermale -0.178 0.837 0.180 0.588 1.191 -0.989 0.323 RaceBlack 0.712 2.039 0.446 0.851 4.887 1.598 0.110 RaceWhite 0.121 1.129 0.355 0.563 2.262 0.341 0.733 Stage2 14.499 1980094.299 1867.955 0.000 Inf 0.008 0.994 Stage3 14.941 3080701.655 1867.955 0.000 Inf 0.008 0.994 Stage4 15.479 5275882.723 1867.955 0.000 Inf 0.008 0.993 Purity 0.111 1.118 0.355 0.557 2.241 0.313 0.754 Rsquare = 0.13 (max possible = 9.91e-01 ) Likelihood ratio test p = 1.95e-07 Wald test p = 1.19e-06 Score (logrank) test p = 3.25e-07 TM7SF2 in BRCA (n=1100): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 878 patients with 121 dying ( 222 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.151 0.860 0.078 0.737 1.002 -1.930 0.054 · Age 0.037 1.037 0.008 1.022 1.053 4.838 0.000 *** Gendermale 0.093 1.097 1.007 0.152 7.898 0.092 0.927 RaceBlack -0.059 0.943 0.620 0.280 3.179 -0.095 0.924 RaceWhite -0.256 0.774 0.596 0.241 2.490 -0.429 0.668 Stage2 0.378 1.460 0.304 0.804 2.651 1.242 0.214 Stage3 1.220 3.387 0.314 1.832 6.261 3.891 0.000 *** Stage4 2.674 14.496 0.397 6.661 31.548 6.739 0.000 *** Purity 0.628 1.874 0.422 0.819 4.288 1.486 0.137 Rsquare = 0.085 (max possible = 7.85e-01 ) Likelihood ratio test p = 4.54e-13 Wald test p = 2.39e-16 Score (logrank) test p = 2.57e-22 TM7SF2 in BRCA-Basal (n=191): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Stage + Purity 163 patients with 24 dying ( 28 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.614 5.410000e-01 0.268 0.320 0.916 -2.288 0.022 * Age 0.007 1.007000e+00 0.018 0.972 1.043 0.364 0.716 RaceBlack -0.958 3.840000e-01 1.110 0.044 3.379 -0.863 0.388 RaceWhite -1.466 2.310000e-01 1.102 0.027 2.003 -1.330 0.184 Stage2 18.418 9.974563e+07 6451.760 0.000 Inf 0.003 0.998 Stage3 20.399 7.234057e+08 6451.760 0.000 Inf 0.003 0.997 Stage4 21.251 1.695616e+09 6451.760 0.000 Inf 0.003 0.997 Purity 1.044 2.839000e+00 0.972 0.422 19.093 1.073 0.283 Rsquare = 0.185 (max possible = 7.18e-01 ) Likelihood ratio test p = 5.18e-05 Wald test p = 3.57e-03 Score (logrank) test p = 8.95e-07 TM7SF2 in BRCA-Her2 (n=82): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Stage + Purity 59 patients with 11 dying ( 23 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.155 1.167000e+00 0.333 0.608 2.242 0.465 0.642 Age 0.040 1.040000e+00 0.032 0.977 1.108 1.236 0.216 RaceBlack -3.197 4.100000e-02 1.838 0.001 1.500 -1.739 0.082 · RaceWhite -1.706 1.820000e-01 1.451 0.011 3.119 -1.176 0.240 Stage2 17.990 6.501118e+07 15244.796 0.000 Inf 0.001 0.999 Stage3 19.634 3.365704e+08 15244.796 0.000 Inf 0.001 0.999 Stage4 52.351 5.443762e+22 1987257.523 0.000 Inf 0.000 1.000 Purity 2.746 1.557300e+01 2.270 0.182 1333.297 1.209 0.227 Rsquare = 0.372 (max possible = 6.68e-01 ) Likelihood ratio test p = 5.85e-04 Wald test p = 1e+00 Score (logrank) test p = 3.88e-14 TM7SF2 in BRCA-LumA (n=568): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 464 patients with 55 dying ( 104 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.001 1.001 0.157 0.735 1.363 0.009 0.993 Age 0.049 1.050 0.012 1.026 1.074 4.090 0.000 *** Gendermale -15.361 0.000 3458.562 0.000 Inf -0.004 0.996 RaceBlack -0.435 0.648 1.175 0.065 6.477 -0.370 0.711 RaceWhite 0.246 1.279 1.031 0.169 9.659 0.239 0.811 Stage2 0.327 1.386 0.374 0.666 2.886 0.873 0.383 Stage3 0.862 2.368 0.395 1.092 5.136 2.183 0.029 * Stage4 2.151 8.592 0.594 2.681 27.537 3.620 0.000 *** Purity 0.306 1.358 0.622 0.402 4.593 0.492 0.622 Rsquare = 0.07 (max possible = 6.81e-01 ) Likelihood ratio test p = 1.04e-04 Wald test p = 2e-05 Score (logrank) test p = 3.73e-07 TM7SF2 in BRCA-LumB (n=219): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 157 patients with 25 dying ( 62 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.186 0.830 0.215 0.544 1.266 -0.866 0.387 Age 0.052 1.054 0.022 1.010 1.099 2.428 0.015 * Gendermale 1.246 3.475 1.154 0.362 33.365 1.079 0.280 RaceBlack 16.481 14371447.569 6525.110 0.000 Inf 0.003 0.998 RaceWhite 15.929 8275759.761 6525.110 0.000 Inf 0.002 0.998 Stage2 0.647 1.910 1.075 0.232 15.702 0.602 0.547 Stage3 1.573 4.820 1.061 0.603 38.536 1.483 0.138 Stage4 2.311 10.080 1.208 0.944 107.639 1.912 0.056 · Purity 1.197 3.309 1.321 0.248 44.072 0.906 0.365 Rsquare = 0.109 (max possible = 6.98e-01 ) Likelihood ratio test p = 3.28e-02 Wald test p = 6.49e-02 Score (logrank) test p = 2.15e-02 TM7SF2 in CESC (n=306): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Purity 227 patients with 55 dying ( 79 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.056 0.945 0.135 0.726 1.231 -0.418 0.676 Age 0.011 1.011 0.010 0.992 1.031 1.148 0.251 RaceBlack 0.998 2.714 1.074 0.331 22.276 0.930 0.353 RaceWhite 0.799 2.223 1.016 0.303 16.297 0.786 0.432 Purity 0.659 1.932 0.764 0.432 8.640 0.862 0.389 Rsquare = 0.014 (max possible = 8.91e-01 ) Likelihood ratio test p = 6.53e-01 Wald test p = 6.85e-01 Score (logrank) test p = 6.77e-01 TM7SF2 in CHOL (n=36): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 36 patients with 18 dying ( 0 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.065 0.937 0.323 0.497 1.766 -0.200 0.841 Age 0.019 1.020 0.023 0.974 1.068 0.826 0.409 Gendermale 0.298 1.348 0.608 0.409 4.439 0.491 0.624 RaceBlack -0.299 0.741 1.514 0.038 14.417 -0.198 0.843 RaceWhite -1.017 0.362 0.937 0.058 2.270 -1.085 0.278 Stage2 0.694 2.002 0.689 0.519 7.717 1.008 0.313 Stage3 -15.469 0.000 6959.967 0.000 Inf -0.002 0.998 Stage4 0.822 2.274 0.668 0.614 8.427 1.229 0.219 Purity 2.166 8.720 1.693 0.316 240.577 1.279 0.201 Rsquare = 0.212 (max possible = 9.46e-01 ) Likelihood ratio test p = 4.76e-01 Wald test p = 6.59e-01 Score (logrank) test p = 4.86e-01 TM7SF2 in COAD (n=458): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 258 patients with 64 dying ( 200 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.029 1.030 0.190 0.710 1.494 0.155 0.877 Age 0.024 1.024 0.011 1.001 1.047 2.079 0.038 * Gendermale 0.214 1.238 0.269 0.731 2.096 0.795 0.427 RaceBlack -0.405 0.667 0.830 0.131 3.392 -0.488 0.626 RaceWhite -0.438 0.645 0.775 0.141 2.949 -0.565 0.572 Stage2 0.210 1.233 0.562 0.410 3.711 0.373 0.709 Stage3 0.798 2.222 0.552 0.753 6.553 1.446 0.148 Stage4 1.882 6.564 0.553 2.220 19.414 3.401 0.001 ** Purity -0.235 0.791 0.605 0.242 2.587 -0.388 0.698 Rsquare = 0.109 (max possible = 9.04e-01 ) Likelihood ratio test p = 4.6e-04 Wald test p = 1.6e-04 Score (logrank) test p = 2.33e-05 TM7SF2 in DLBC (n=48): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 41 patients with 5 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.582 0.559 0.844 0.107 2.922 -0.690 0.490 Age 0.000 1.000 0.042 0.920 1.086 -0.006 0.995 Gendermale 0.676 1.966 1.082 0.236 16.396 0.625 0.532 RaceBlack 0.780 2.182 1.707 0.077 61.963 0.457 0.648 RaceWhite -2.304 0.100 1.340 0.007 1.381 -1.719 0.086 · Purity -2.027 0.132 2.202 0.002 9.868 -0.920 0.357 Rsquare = 0.141 (max possible = 5.58e-01 ) Likelihood ratio test p = 3.98e-01 Wald test p = 5.65e-01 Score (logrank) test p = 3.2e-01 TM7SF2 in ESCA (n=185): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 140 patients with 47 dying ( 45 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.098 0.906 0.156 0.668 1.231 -0.629 0.529 Age 0.011 1.012 0.014 0.983 1.041 0.794 0.427 Gendermale 0.473 1.604 0.538 0.559 4.605 0.878 0.380 RaceBlack 0.405 1.500 1.073 0.183 12.274 0.378 0.706 RaceWhite -0.108 0.897 0.452 0.370 2.176 -0.240 0.811 Stage2 0.692 1.998 0.656 0.552 7.227 1.055 0.291 Stage3 1.440 4.220 0.671 1.133 15.722 2.146 0.032 * Stage4 2.775 16.045 0.788 3.421 75.245 3.520 0.000 *** Purity 0.302 1.352 0.788 0.289 6.334 0.383 0.702 Rsquare = 0.143 (max possible = 9.32e-01 ) Likelihood ratio test p = 1e-02 Wald test p = 4.06e-03 Score (logrank) test p = 3.46e-04 TM7SF2 in GBM (n=153): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 135 patients with 108 dying ( 18 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.029 0.971 0.143 0.735 1.285 -0.204 0.838 Age 0.030 1.030 0.008 1.014 1.047 3.577 0.000 *** Gendermale -0.102 0.903 0.215 0.592 1.378 -0.472 0.637 RaceBlack 0.529 1.697 0.726 0.409 7.047 0.728 0.467 RaceWhite -0.234 0.791 0.615 0.237 2.642 -0.380 0.704 Purity -1.069 0.343 0.540 0.119 0.990 -1.979 0.048 * Rsquare = 0.129 (max possible = 9.98e-01 ) Likelihood ratio test p = 4.73e-03 Wald test p = 6.99e-03 Score (logrank) test p = 6e-03 TM7SF2 in HNSC (n=522): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 411 patients with 176 dying ( 111 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.051 0.950 0.064 0.839 1.076 -0.810 0.418 Age 0.023 1.023 0.008 1.008 1.038 2.946 0.003 ** Gendermale -0.243 0.785 0.172 0.560 1.099 -1.410 0.159 RaceBlack 0.182 1.199 0.562 0.399 3.607 0.323 0.747 RaceWhite -0.235 0.791 0.511 0.290 2.154 -0.459 0.646 Stage2 0.618 1.856 0.544 0.639 5.387 1.137 0.255 Stage3 0.859 2.360 0.537 0.824 6.758 1.599 0.110 Stage4 1.259 3.521 0.510 1.296 9.568 2.468 0.014 * Purity -0.051 0.950 0.363 0.466 1.936 -0.142 0.887 Rsquare = 0.071 (max possible = 9.89e-01 ) Likelihood ratio test p = 4.07e-04 Wald test p = 1.12e-03 Score (logrank) test p = 8.31e-04 TM7SF2 in HNSC-HPV+ (n=98): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 65 patients with 23 dying ( 33 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.225 7.990000e-01 0.224 0.515 1.238 -1.004 0.315 Age 0.009 1.009000e+00 0.026 0.960 1.061 0.367 0.714 Gendermale 0.028 1.028000e+00 0.569 0.337 3.140 0.049 0.961 RaceBlack 19.086 1.944906e+08 12161.402 0.000 Inf 0.002 0.999 RaceWhite 18.162 7.720261e+07 12161.402 0.000 Inf 0.001 0.999 Stage2 17.358 3.454218e+07 5300.704 0.000 Inf 0.003 0.997 Stage3 16.520 1.494526e+07 5300.704 0.000 Inf 0.003 0.998 Stage4 17.326 3.347770e+07 5300.704 0.000 Inf 0.003 0.997 Purity -1.604 2.010000e-01 1.078 0.024 1.663 -1.488 0.137 Rsquare = 0.101 (max possible = 9.17e-01 ) Likelihood ratio test p = 6.44e-01 Wald test p = 9.12e-01 Score (logrank) test p = 7.99e-01 TM7SF2 in HNSC-HPV- (n=422): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 346 patients with 153 dying ( 76 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.051 0.951 0.067 0.833 1.084 -0.755 0.451 Age 0.027 1.028 0.008 1.011 1.045 3.237 0.001 ** Gendermale -0.285 0.752 0.183 0.526 1.076 -1.559 0.119 RaceBlack 0.021 1.022 0.567 0.337 3.101 0.038 0.970 RaceWhite -0.391 0.676 0.513 0.248 1.847 -0.763 0.445 Stage2 0.371 1.449 0.554 0.489 4.291 0.670 0.503 Stage3 0.737 2.090 0.541 0.724 6.038 1.362 0.173 Stage4 1.159 3.188 0.512 1.168 8.703 2.263 0.024 * Purity 0.216 1.241 0.400 0.567 2.718 0.540 0.589 Rsquare = 0.086 (max possible = 9.89e-01 ) Likelihood ratio test p = 2.72e-04 Wald test p = 8.01e-04 Score (logrank) test p = 6.11e-04 TM7SF2 in KICH (n=66): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 63 patients with 9 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z TM7SF2 -1.452 2.340000e-01 0.590 7.400000e-02 7.440000e-01 -2.460 Age 0.072 1.075000e+00 0.029 1.015000e+00 1.138000e+00 2.481 Gendermale -1.447 2.350000e-01 0.751 5.400000e-02 1.025000e+00 -1.927 RaceBlack -18.908 0.000000e+00 12006.989 0.000000e+00 Inf -0.002 RaceWhite -3.497 3.000000e-02 1.221 3.000000e-03 3.310000e-01 -2.865 Stage2 18.199 8.010916e+07 0.856 1.495119e+07 4.292285e+08 21.249 Stage3 19.689 3.554806e+08 0.794 7.492392e+07 1.686597e+09 24.785 Stage4 21.232 1.662783e+09 0.948 2.594993e+08 1.065455e+10 22.403 Purity 4.591 9.862200e+01 4.865 7.000000e-03 1.364960e+06 0.944 p signif TM7SF2 0.014 * Age 0.013 * Gendermale 0.054 · RaceBlack 0.999 RaceWhite 0.004 ** Stage2 0.000 *** Stage3 0.000 *** Stage4 0.000 *** Purity 0.345 Rsquare = 0.409 (max possible = 6.71e-01 ) Likelihood ratio test p = 1.26e-04 Wald test p = 0e+00 Score (logrank) test p = 1.36e-09 TM7SF2 in KIRC (n=533): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 450 patients with 138 dying ( 83 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.139 0.871 0.097 0.719 1.053 -1.425 0.154 Age 0.035 1.036 0.008 1.019 1.053 4.170 0.000 *** Gendermale -0.078 0.925 0.184 0.645 1.327 -0.422 0.673 RaceBlack 0.295 1.343 1.060 0.168 10.717 0.278 0.781 RaceWhite 0.173 1.189 1.015 0.163 8.697 0.171 0.864 Stage2 0.207 1.230 0.344 0.627 2.413 0.601 0.548 Stage3 0.765 2.149 0.232 1.363 3.388 3.293 0.001 ** Stage4 1.707 5.514 0.219 3.591 8.466 7.803 0.000 *** Purity 0.115 1.122 0.375 0.538 2.339 0.308 0.758 Rsquare = 0.178 (max possible = 9.65e-01 ) Likelihood ratio test p = 4.13e-15 Wald test p = 2.43e-15 Score (logrank) test p = 1.76e-18 TM7SF2 in KIRP (n=290): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 219 patients with 34 dying ( 71 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.313 0.731 0.185 0.509 1.051 -1.690 0.091 · Age 0.009 1.009 0.015 0.979 1.039 0.553 0.580 Gendermale -0.363 0.695 0.394 0.321 1.506 -0.922 0.357 RaceBlack -1.896 0.150 1.198 0.014 1.572 -1.583 0.114 RaceWhite -2.115 0.121 1.177 0.012 1.212 -1.797 0.072 · Stage2 -0.427 0.653 1.054 0.083 5.155 -0.405 0.686 Stage3 1.485 4.414 0.436 1.880 10.365 3.409 0.001 ** Stage4 2.644 14.067 0.512 5.153 38.399 5.160 0.000 *** Purity -0.210 0.810 0.743 0.189 3.478 -0.283 0.777 Rsquare = 0.173 (max possible = 7.58e-01 ) Likelihood ratio test p = 3.95e-06 Wald test p = 9.69e-07 Score (logrank) test p = 1.41e-10 TM7SF2 in LAML (n=173): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race 149 patients with 93 dying ( 24 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.169 1.185 0.163 0.860 1.631 1.038 0.299 Age 0.039 1.040 0.008 1.023 1.056 4.735 0.000 *** Gendermale -0.172 0.842 0.215 0.553 1.283 -0.801 0.423 RaceBlack -0.334 0.716 1.105 0.082 6.253 -0.302 0.763 RaceWhite -0.680 0.507 1.018 0.069 3.726 -0.668 0.504 Rsquare = 0.162 (max possible = 9.96e-01 ) Likelihood ratio test p = 7.84e-05 Wald test p = 2.71e-04 Score (logrank) test p = 1.92e-04 TM7SF2 in LGG (n=516): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 463 patients with 112 dying ( 53 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.961 0.383 0.158 0.281 0.521 -6.087 0.000 *** Age 0.067 1.070 0.008 1.053 1.087 8.424 0.000 *** Gendermale -0.002 0.998 0.196 0.680 1.467 -0.008 0.994 RaceBlack 15.268 4273495.050 2270.803 0.000 Inf 0.007 0.995 RaceWhite 15.542 5620807.704 2270.803 0.000 Inf 0.007 0.995 Purity -0.855 0.425 0.406 0.192 0.942 -2.107 0.035 * Rsquare = 0.199 (max possible = 9.07e-01 ) Likelihood ratio test p = 6.86e-20 Wald test p = 1.3e-19 Score (logrank) test p = 6.86e-22 TM7SF2 in LIHC (n=371): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 310 patients with 106 dying ( 61 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.056 0.946 0.080 0.808 1.108 -0.690 0.490 Age 0.011 1.011 0.008 0.995 1.027 1.390 0.164 Gendermale -0.119 0.888 0.227 0.569 1.386 -0.523 0.601 RaceBlack 0.904 2.469 0.489 0.947 6.437 1.849 0.064 · RaceWhite 0.001 1.001 0.237 0.630 1.593 0.005 0.996 Stage2 0.297 1.346 0.262 0.806 2.250 1.135 0.257 Stage3 0.934 2.544 0.236 1.601 4.041 3.954 0.000 *** Stage4 1.569 4.804 0.619 1.427 16.167 2.535 0.011 * Purity 0.661 1.936 0.474 0.765 4.903 1.394 0.163 Rsquare = 0.086 (max possible = 9.66e-01 ) Likelihood ratio test p = 1.01e-03 Wald test p = 6.25e-04 Score (logrank) test p = 2.3e-04 TM7SF2 in LUAD (n=515): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 421 patients with 150 dying ( 94 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.095 0.909 0.092 0.760 1.089 -1.035 0.301 Age 0.007 1.007 0.009 0.989 1.025 0.759 0.448 Gendermale 0.023 1.023 0.169 0.735 1.424 0.134 0.894 RaceBlack 16.081 9632133.185 1899.155 0.000 Inf 0.008 0.993 RaceWhite 16.259 11513401.536 1899.155 0.000 Inf 0.009 0.993 Stage2 0.867 2.381 0.201 1.606 3.530 4.316 0.000 *** Stage3 1.036 2.818 0.219 1.835 4.328 4.733 0.000 *** Stage4 0.986 2.682 0.335 1.392 5.166 2.949 0.003 ** Purity 0.694 2.001 0.355 0.998 4.011 1.955 0.051 · Rsquare = 0.099 (max possible = 9.74e-01 ) Likelihood ratio test p = 1.53e-06 Wald test p = 2.07e-05 Score (logrank) test p = 2.25e-06 TM7SF2 in LUSC (n=501): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 370 patients with 161 dying ( 131 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.014 1.014 0.083 0.862 1.192 0.167 0.868 Age 0.016 1.016 0.009 0.998 1.035 1.746 0.081 · Gendermale 0.437 1.548 0.194 1.059 2.262 2.258 0.024 * RaceBlack 0.009 1.009 0.606 0.307 3.312 0.014 0.989 RaceWhite -0.519 0.595 0.563 0.197 1.795 -0.921 0.357 Stage2 0.211 1.234 0.187 0.856 1.780 1.128 0.259 Stage3 0.602 1.826 0.214 1.200 2.780 2.809 0.005 ** Stage4 0.761 2.141 0.795 0.451 10.167 0.958 0.338 Purity -0.350 0.705 0.366 0.344 1.444 -0.956 0.339 Rsquare = 0.05 (max possible = 9.87e-01 ) Likelihood ratio test p = 2.4e-02 Wald test p = 1.86e-02 Score (logrank) test p = 1.61e-02 TM7SF2 in MESO (n=87): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 85 patients with 72 dying ( 2 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.143 1.154 0.152 0.858 1.553 0.946 0.344 Age 0.020 1.020 0.016 0.989 1.052 1.283 0.200 Gendermale -0.207 0.813 0.329 0.427 1.549 -0.630 0.529 RaceBlack 0.232 1.261 1.530 0.063 25.282 0.151 0.880 RaceWhite -0.275 0.759 1.071 0.093 6.197 -0.257 0.797 Stage2 -0.268 0.765 0.466 0.307 1.908 -0.574 0.566 Stage3 -0.063 0.939 0.419 0.413 2.133 -0.151 0.880 Stage4 -0.008 0.992 0.495 0.376 2.620 -0.016 0.988 Purity -0.920 0.398 0.580 0.128 1.243 -1.586 0.113 Rsquare = 0.07 (max possible = 9.98e-01 ) Likelihood ratio test p = 7.25e-01 Wald test p = 7.17e-01 Score (logrank) test p = 7.06e-01 TM7SF2 in OV (n=303): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Purity 238 patients with 153 dying ( 65 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.177 0.838 0.092 0.699 1.004 -1.919 0.055 · Age 0.039 1.039 0.008 1.023 1.056 4.730 0.000 *** RaceBlack 0.081 1.085 0.582 0.347 3.396 0.140 0.889 RaceWhite -0.053 0.948 0.518 0.344 2.618 -0.102 0.919 Purity -0.576 0.562 0.682 0.148 2.139 -0.845 0.398 Rsquare = 0.095 (max possible = 9.97e-01 ) Likelihood ratio test p = 2.4e-04 Wald test p = 1.87e-04 Score (logrank) test p = 1.5e-04 TM7SF2 in PAAD (n=179): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 165 patients with 89 dying ( 14 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.117 0.889 0.153 0.659 1.199 -0.769 0.442 Age 0.023 1.023 0.011 1.001 1.045 2.093 0.036 * Gendermale -0.242 0.785 0.220 0.510 1.207 -1.103 0.270 RaceBlack -0.103 0.902 0.747 0.209 3.900 -0.138 0.890 RaceWhite 0.313 1.367 0.478 0.536 3.485 0.654 0.513 Stage2 0.575 1.777 0.441 0.749 4.216 1.305 0.192 Stage3 -0.280 0.756 1.092 0.089 6.422 -0.257 0.798 Stage4 0.228 1.256 0.823 0.250 6.306 0.277 0.782 Purity -0.620 0.538 0.412 0.240 1.206 -1.505 0.132 Rsquare = 0.092 (max possible = 9.91e-01 ) Likelihood ratio test p = 6.95e-02 Wald test p = 1.04e-01 Score (logrank) test p = 9.7e-02 TM7SF2 in PCPG (n=181): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 164 patients with 7 dying ( 17 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.622 1.862000e+00 0.527 0.664 5.227 1.181 0.238 Age 0.047 1.048000e+00 0.030 0.988 1.112 1.546 0.122 Gendermale 1.437 4.207000e+00 0.885 0.742 23.846 1.623 0.105 RaceBlack -0.838 4.330000e-01 29746.121 0.000 Inf 0.000 1.000 RaceWhite 18.515 1.098808e+08 25405.004 0.000 Inf 0.001 0.999 Purity 5.827 3.394320e+02 3.481 0.369 311869.212 1.674 0.094 · Rsquare = 0.063 (max possible = 3.07e-01 ) Likelihood ratio test p = 9.72e-02 Wald test p = 4.41e-01 Score (logrank) test p = 2.75e-01 TM7SF2 in PRAD (n=498): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Purity 403 patients with 9 dying ( 95 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.439 1.551 0.517 0.563 4.272 0.850 0.396 Age 0.004 1.004 0.058 0.897 1.124 0.070 0.944 RaceBlack 15.104 3625624.231 6675.348 0.000 Inf 0.002 0.998 RaceWhite 16.473 14265025.436 6675.348 0.000 Inf 0.002 0.998 Purity 0.893 2.441 1.436 0.146 40.751 0.621 0.534 Rsquare = 0.008 (max possible = 1.83e-01 ) Likelihood ratio test p = 6.35e-01 Wald test p = 7.6e-01 Score (logrank) test p = 7.06e-01 TM7SF2 in READ (n=166): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 76 patients with 14 dying ( 90 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.152 1.165 0.578 0.375 3.615 0.264 0.792 Age 0.107 1.113 0.045 1.020 1.215 2.409 0.016 * Gendermale -0.312 0.732 0.705 0.184 2.914 -0.443 0.658 RaceBlack 13.465 703978.830 10153.248 0.000 Inf 0.001 0.999 RaceWhite 12.530 276540.820 10153.248 0.000 Inf 0.001 0.999 Stage2 -1.833 0.160 1.255 0.014 1.873 -1.460 0.144 Stage3 -0.479 0.619 0.908 0.104 3.672 -0.528 0.598 Stage4 -0.146 0.865 0.957 0.133 5.636 -0.152 0.879 Purity -0.049 0.952 1.498 0.051 17.944 -0.033 0.974 Rsquare = 0.21 (max possible = 7.22e-01 ) Likelihood ratio test p = 3.62e-02 Wald test p = 2.26e-01 Score (logrank) test p = 4.64e-02 TM7SF2 in SARC (n=260): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 233 patients with 90 dying ( 27 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.122 1.130 0.107 0.916 1.394 1.143 0.253 Age 0.024 1.024 0.008 1.008 1.041 2.910 0.004 ** Gendermale 0.010 1.010 0.223 0.653 1.563 0.045 0.964 RaceBlack -0.045 0.956 1.089 0.113 8.077 -0.041 0.967 RaceWhite -0.372 0.689 1.026 0.092 5.146 -0.363 0.717 Purity 0.850 2.341 0.576 0.756 7.245 1.475 0.140 Rsquare = 0.048 (max possible = 9.75e-01 ) Likelihood ratio test p = 7.76e-02 Wald test p = 9.46e-02 Score (logrank) test p = 9.36e-02 TM7SF2 in SKCM (n=471): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 390 patients with 186 dying ( 81 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.217 1.242 0.066 1.090 1.415 3.262 0.001 ** Age 0.017 1.017 0.005 1.007 1.028 3.297 0.001 ** Gendermale -0.068 0.934 0.158 0.686 1.272 -0.433 0.665 RaceWhite -1.192 0.303 0.401 0.138 0.666 -2.973 0.003 ** Stage2 0.285 1.330 0.218 0.867 2.040 1.307 0.191 Stage3 0.678 1.969 0.205 1.317 2.943 3.304 0.001 ** Stage4 1.431 4.182 0.354 2.088 8.374 4.038 0.000 *** Purity 1.086 2.964 0.335 1.538 5.709 3.247 0.001 ** Rsquare = 0.146 (max possible = 9.92e-01 ) Likelihood ratio test p = 2.28e-10 Wald test p = 2.14e-10 Score (logrank) test p = 1.77e-11 TM7SF2 in SKCM-Primary (n=103): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 94 patients with 26 dying ( 9 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.207 1.230000e+00 0.163 0.893 1.693 1.267 0.205 Age 0.014 1.014000e+00 0.016 0.982 1.046 0.840 0.401 Gendermale 0.362 1.436000e+00 0.454 0.590 3.494 0.798 0.425 RaceWhite -1.141 3.200000e-01 0.630 0.093 1.099 -1.810 0.070 · Stage2 17.448 3.779685e+07 6238.521 0.000 Inf 0.003 0.998 Stage3 18.116 7.376554e+07 6238.521 0.000 Inf 0.003 0.998 Stage4 20.053 5.115801e+08 6238.521 0.000 Inf 0.003 0.997 Purity 0.466 1.593000e+00 0.942 0.251 10.099 0.494 0.621 Rsquare = 0.161 (max possible = 8.69e-01 ) Likelihood ratio test p = 3.61e-02 Wald test p = 2.93e-02 Score (logrank) test p = 2.23e-03 TM7SF2 in SKCM-Metastasis (n=368): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 296 patients with 160 dying ( 72 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.204 1.226 0.073 1.062 1.415 2.776 0.006 ** Age 0.019 1.019 0.006 1.008 1.031 3.405 0.001 ** Gendermale -0.090 0.914 0.173 0.651 1.284 -0.517 0.605 RaceWhite -1.018 0.361 0.600 0.112 1.170 -1.698 0.090 · Stage2 0.172 1.188 0.230 0.757 1.865 0.749 0.454 Stage3 0.620 1.858 0.210 1.231 2.805 2.948 0.003 ** Stage4 1.225 3.404 0.403 1.546 7.495 3.041 0.002 ** Purity 1.193 3.296 0.365 1.613 6.739 3.270 0.001 ** Rsquare = 0.155 (max possible = 9.95e-01 ) Likelihood ratio test p = 4.28e-08 Wald test p = 1.14e-07 Score (logrank) test p = 3.16e-08 TM7SF2 in STAD (n=415): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 289 patients with 111 dying ( 126 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.051 1.052 0.104 0.859 1.289 0.489 0.625 Age 0.026 1.026 0.010 1.006 1.047 2.554 0.011 * Gendermale 0.113 1.120 0.208 0.745 1.684 0.544 0.587 RaceBlack 0.256 1.292 0.448 0.537 3.111 0.572 0.567 RaceWhite 0.094 1.098 0.244 0.681 1.771 0.384 0.701 Stage2 0.489 1.630 0.390 0.759 3.501 1.254 0.210 Stage3 0.917 2.503 0.364 1.227 5.106 2.522 0.012 * Stage4 1.319 3.738 0.505 1.390 10.051 2.613 0.009 ** Purity -0.594 0.552 0.390 0.257 1.185 -1.524 0.128 Rsquare = 0.07 (max possible = 9.79e-01 ) Likelihood ratio test p = 1.26e-02 Wald test p = 1.83e-02 Score (logrank) test p = 1.49e-02 TM7SF2 in TGCT (n=150): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Stage + Purity 75 patients with 2 dying ( 75 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 4.026 56.058 18283.156 0 Inf 0.000 1.000 Age -1.922 0.146 1649.426 0 Inf -0.001 0.999 RaceBlack 5.542 255.104 19794589.403 0 Inf 0.000 1.000 RaceWhite -40.264 0.000 19632695.165 0 Inf 0.000 1.000 Stage2 1.089 2.971 40583.876 0 Inf 0.000 1.000 Stage3 12.438 252308.413 149901.633 0 Inf 0.000 1.000 Purity -8.001 0.000 222842.015 0 Inf 0.000 1.000 Rsquare = 0.14 (max possible = 1.4e-01 ) Likelihood ratio test p = 1.25e-01 Wald test p = 1e+00 Score (logrank) test p = 3.01e-03 TM7SF2 in THCA (n=509): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Stage + Purity 395 patients with 16 dying ( 114 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.314 1.369 0.418 0.603 3.105 0.751 0.453 Age 0.152 1.164 0.030 1.098 1.233 5.123 0.000 *** Gendermale -0.047 0.954 0.639 0.273 3.338 -0.074 0.941 RaceBlack 17.698 48529138.543 9370.664 0.000 Inf 0.002 0.998 RaceWhite 17.599 43990506.632 9370.664 0.000 Inf 0.002 0.999 Stage2 -0.541 0.582 1.168 0.059 5.741 -0.464 0.643 Stage3 0.389 1.476 0.876 0.265 8.217 0.444 0.657 Stage4 1.772 5.880 1.001 0.827 41.827 1.770 0.077 · Purity 2.138 8.485 1.096 0.990 72.708 1.951 0.051 · Rsquare = 0.15 (max possible = 3.47e-01 ) Likelihood ratio test p = 1.96e-10 Wald test p = 5.74e-04 Score (logrank) test p = 1.22e-10 TM7SF2 in THYM (n=120): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Race + Purity 113 patients with 9 dying ( 7 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.477 1.612 0.506 0.598 4.345 0.943 0.346 Age 0.057 1.058 0.033 0.992 1.129 1.708 0.088 · Gendermale -0.097 0.907 0.742 0.212 3.888 -0.131 0.896 RaceBlack -16.891 0.000 10390.991 0.000 Inf -0.002 0.999 RaceWhite 0.163 1.177 1.124 0.130 10.651 0.145 0.885 Purity 0.288 1.334 1.122 0.148 12.038 0.257 0.797 Rsquare = 0.051 (max possible = 4.51e-01 ) Likelihood ratio test p = 4.31e-01 Wald test p = 5.35e-01 Score (logrank) test p = 4.25e-01 TM7SF2 in UCEC (n=545): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Purity 283 patients with 43 dying ( 262 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 0.097 1.101 0.163 0.800 1.516 0.593 0.553 Age 0.050 1.052 0.016 1.019 1.085 3.166 0.002 ** RaceBlack -0.467 0.627 0.799 0.131 3.001 -0.584 0.559 RaceWhite -0.575 0.563 0.749 0.130 2.443 -0.767 0.443 Purity 0.358 1.430 0.659 0.393 5.198 0.543 0.587 Rsquare = 0.04 (max possible = 7.81e-01 ) Likelihood ratio test p = 4.39e-02 Wald test p = 5.34e-02 Score (logrank) test p = 5.18e-02 TM7SF2 in UCS (n=57): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Race + Purity 52 patients with 32 dying ( 5 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.042 0.959 0.174 0.681 1.349 -0.242 0.809 Age 0.046 1.047 0.025 0.996 1.100 1.795 0.073 · RaceBlack 17.588 43499837.130 6478.860 0.000 Inf 0.003 0.998 RaceWhite 17.882 58367483.283 6478.860 0.000 Inf 0.003 0.998 Purity -0.911 0.402 1.073 0.049 3.293 -0.849 0.396 Rsquare = 0.12 (max possible = 9.83e-01 ) Likelihood ratio test p = 2.49e-01 Wald test p = 3.55e-01 Score (logrank) test p = 2.62e-01 TM7SF2 in UVM (n=80): Model: Surv(OS, EVENT) ~ `TM7SF2` + Age + Gender + Stage + Purity 77 patients with 22 dying ( 3 missing obs. ) coef HR se(coef) 95%CI_l 95%CI_u z p signif TM7SF2 -0.662 0.516 0.331 0.270 0.986 -2.002 0.045 * Age 0.045 1.047 0.021 1.005 1.089 2.217 0.027 * Gendermale 0.189 1.208 0.493 0.460 3.174 0.384 0.701 Stage3 0.422 1.525 0.510 0.561 4.141 0.827 0.408 Stage4 3.802 44.784 1.225 4.056 494.468 3.103 0.002 ** Purity 1.742 5.707 1.289 0.456 71.407 1.351 0.177 Rsquare = 0.295 (max possible = 8.72e-01 ) Likelihood ratio test p = 1.52e-04 Wald test p = 1.17e-03 Score (logrank) test p = 6.22e-10