Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

ABSTRACT:

Aim

In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer.

Method

We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032.

Results

FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7285394 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature