TCGA生存分析-单基因

01

> library(RTCGA)

> infoTCGA <- infoTCGA()

> View(infoTCGA)

> library(RTCGA.clinical)

> clin <- survivalTCGA(BRCA.clinical)

> class(clin)

[1] "data.frame"

> head(clin)

times bcr_patient_barcode patient.vital_status

1 3767 TCGA-3C-AAAU 0

2 3801 TCGA-3C-AALI 0

3 1228 TCGA-3C-AALJ 0

4 1217 TCGA-3C-AALK 0

5 158 TCGA-4H-AAAK 0

6 1477 TCGA-5L-AAT0 0

> library(RTCGA.mRNA)

> class(BRCA.mRNA)

[1] "data.frame"

> dim(BRCA.mRNA)

[1] 590 17815

> BRCA.mRNA[1:5,1:5]

bcr_patient_barcode ELMO2 CREB3L1 RPS11 PNMA1

1 TCGA-A1-A0SD-01A-11R-A115-07 0.5070833 1.43450 0.765000 0.52600

2 TCGA-A1-A0SE-01A-11R-A084-07 0.1814167 0.89075 0.716000 0.13175

3 TCGA-A1-A0SH-01A-11R-A084-07 0.4615000 2.25925 0.417125 0.32500

4 TCGA-A1-A0SJ-01A-11R-A084-07 0.8770000 0.43775 0.115000 0.75775

5 TCGA-A1-A0SK-01A-12R-A084-07 1.4123333 -0.63725 0.492875 0.94325

> library(dplyr)

> exprSet <- BRCA.mRNA %>%

+ as_tibble() %>%

+select(bcr_patient_barcode,PAX8,GATA3,ESR1) %>%

+mutate(bcr_patient_barcode=substr(bcr_patient_barcode,1,12)) %>%

+ inner_join(clin,by="bcr_patient_barcode")

> library(survival)

> library(survminer)

> group <-ifelse(exprSet

> sfit <-survfit(Surv(times,patient.vital_status)~group,data=exprSet)

> sfit

Call: survfit(formula = Surv(times, patient.vital_status) ~ group,

data = exprSet)

n events median 0.95LCL 0.95UCL

group=high 295 35 3462 2965 NA

group=low 295 46 2763 2207 NA

> summary(sfit)

Call: survfit(formula = Surv(times, patient.vital_status) ~ group,

data = exprSet)

group=high

time n.risk n.event survival std.err lower 95% CI upper 95% CI

158 254 1 0.996 0.00393 0.988 1.000

160 253 1 0.992 0.00555 0.981 1.000

224 237 1 0.988 0.00692 0.974 1.000

362 207 1 0.983 0.00838 0.967 1.000

365 206 1 0.978 0.00960 0.960 0.997

558 162 1 0.972 0.01128 0.950 0.995

612 152 1 0.966 0.01289 0.941 0.992

825 131 1 0.959 0.01475 0.930 0.988

860 123 1 0.951 0.01656 0.919 0.984

883 120 1 0.943 0.01822 0.908 0.979

921 113 1 0.935 0.01988 0.896 0.974

943 112 1 0.926 0.02138 0.885 0.969

991 107 1 0.918 0.02287 0.874 0.963

1127 101 1 0.908 0.02438 0.862 0.958

1142 99 1 0.899 0.02580 0.850 0.951

1148 98 1 0.890 0.02712 0.838 0.945

1542 61 1 0.875 0.03035 0.818 0.937

1563 58 1 0.860 0.03337 0.797 0.928

1781 51 1 0.844 0.03673 0.775 0.919

1920 46 1 0.825 0.04025 0.750 0.908

2009 44 1 0.806 0.04349 0.726 0.896

2097 41 1 0.787 0.04666 0.700 0.884

2373 34 1 0.764 0.05070 0.670 0.870

2417 32 1 0.740 0.05445 0.640 0.855

2469 30 1 0.715 0.05795 0.610 0.838

2483 29 1 0.690 0.06097 0.581 0.821

2520 27 1 0.665 0.06385 0.551 0.803

2551 26 1 0.639 0.06632 0.522 0.783

2965 20 1 0.607 0.07028 0.484 0.762

3126 18 1 0.574 0.07404 0.445 0.739

3418 14 1 0.533 0.07928 0.398 0.713

3462 13 1 0.492 0.08310 0.353 0.685

3941 11 1 0.447 0.08673 0.306 0.654

3945 9 1 0.397 0.09020 0.255 0.620

4456 8 1 0.348 0.09158 0.207 0.583

group=low

time n.risk n.event survival std.err lower 95% CI upper 95% CI

255 226 1 0.996 0.00441 0.987 1.000

304 214 1 0.991 0.00639 0.978 1.000

426 189 1 0.986 0.00823 0.970 1.000

524 171 1 0.980 0.01000 0.961 1.000

548 168 1 0.974 0.01152 0.952 0.997

571 166 1 0.968 0.01286 0.943 0.994

612 157 1 0.962 0.01418 0.935 0.990

639 154 1 0.956 0.01540 0.926 0.986

723 143 1 0.949 0.01668 0.917 0.982

749 138 1 0.942 0.01792 0.908 0.978

754 137 1 0.935 0.01906 0.899 0.973

785 128 2 0.921 0.02138 0.880 0.964

811 126 2 0.906 0.02341 0.861 0.953

921 119 1 0.899 0.02442 0.852 0.948

967 115 1 0.891 0.02543 0.842 0.942

991 113 1 0.883 0.02639 0.833 0.936

1142 102 1 0.874 0.02752 0.822 0.930

1148 101 1 0.866 0.02857 0.811 0.923

1272 90 1 0.856 0.02983 0.799 0.916

1286 89 2 0.837 0.03211 0.776 0.902

1365 75 1 0.826 0.03357 0.762 0.894

1556 58 2 0.797 0.03797 0.726 0.875

1563 55 1 0.783 0.03995 0.708 0.865

1692 47 2 0.749 0.04465 0.667 0.842

1694 45 2 0.716 0.04848 0.627 0.818

1699 43 1 0.699 0.05013 0.608 0.805

1793 39 1 0.681 0.05195 0.587 0.791

1993 30 1 0.659 0.05496 0.559 0.776

2009 29 1 0.636 0.05757 0.533 0.759

2207 27 2 0.589 0.06220 0.479 0.724

2520 24 1 0.564 0.06426 0.451 0.705

2573 22 1 0.539 0.06626 0.423 0.686

2763 19 2 0.482 0.07038 0.362 0.642

2798 17 2 0.425 0.07263 0.304 0.594

3063 13 1 0.393 0.07404 0.271 0.568

3461 10 1 0.353 0.07634 0.231 0.540

4267 6 1 0.294 0.08328 0.169 0.513

> ggsurvplot(sfit,conf.int = FALSE,pval = TRUE)