how to do gene mutation multivariate survival analysis?
0
0
Entering edit mode
5.0 years ago
jnmcluo ▴ 20

I've been working on gene mutation survival analysis, the data downloaded&merged from TCGA somatic mutation file (MAF) is:

data of clinical feature and mutation genes SRCAP ZFHX4 AMER1 PCDHB8 AHNAK2 ... are genes selected by the univariate KM survival& log-rank test, by dividing patient to Wt and Mutate group based on gene mutate status and then order the p-values, choose p=0.05 as the threshold. Now I need to take account of all clinical features into the analysis along with these genes:

Surv(futime, fustat)~ gender+age+project+subtype+race_group+stage_group+SRCAP+ZFHX4+AMER1+PCDHB8+AHNAK2+DNAH5+NALCN+PAPPA+PCDH17+RELN+UGGT2+HYDIN

and the result:

                      coef  exp(coef)   se(coef)  robust se      z Pr(>|z|)    
genderMALE       9.020e-01  2.465e+00  3.819e-01  3.696e-01  2.441 0.014659 *  
subtypeMissing   4.793e-01  1.615e+00  8.825e-01  1.045e+00  0.459 0.646364    
subtypeMucinous  1.354e+00  3.874e+00  5.972e-01  6.053e-01  2.238 0.025250 *  
race_groupWhite -6.223e-01  5.367e-01  3.921e-01  3.903e-01 -1.594 0.110878    
SRCAPWT         -1.233e+00  2.914e-01  5.177e-01  6.516e-01 -1.892 0.058474 .  
ZFHX4WT         -1.577e+00  2.065e-01  4.996e-01  5.621e-01 -2.806 0.005014 ** 
AMER1WT         -2.932e+00  5.332e-02  6.121e-01  5.547e-01 -5.285 1.26e-07 ***
AHNAK2WT         2.190e+00  8.932e+00  1.063e+00  9.183e-01  2.385 0.017097 *  
DNAH5WT          2.011e+00  7.474e+00  7.732e-01  6.077e-01  3.310 0.000932 ***
NALCNWT         -8.528e-01  4.262e-01  4.790e-01  4.151e-01 -2.055 0.039905 *  
RELNWT           2.063e+01  9.155e+08  5.425e+03  1.659e+00 12.435  < 2e-16 ***
UGGT2WT         -2.783e+00  6.185e-02  7.052e-01  5.688e-01 -4.893 9.95e-07 ***
HYDINWT          1.864e+00  6.450e+00  7.435e-01  7.284e-01  2.559 0.010499 *

I'm not convinced about the whole procedure and the result, how the "Stage" factor is not important to survival chance? besides, some gene's hazard ratio is incredible high(RELNWT :9.155e+08 ) . not sure if the reason is the sparse & binary feature of mutation data.

what's is the proper way to preform survival analysis based on mutation data? really need an explanation....thanks.

maf mutation coxph • 1.2k views
ADD COMMENT

Login before adding your answer.

Traffic: 1645 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6