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

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

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.

FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 - 0.737∗TACR2 - 0.651∗PLEK2 - 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups.

A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.