Abstract
DNA repair genes can be used as prognostic biomarkers in many types of cancer. We aimed to identify prognostic DNA repair genes in patients with gastric cancer (GC) by systematically bioinformatic approaches using web-based database. Global gene expression profiles from altogether 1,325 GC patients' samples from six independent datasets were included in the study. Clustering analysis was performed to screen potentially abnormal DNA repair genes related to the prognosis of GC, followed by unsupervised clustering analysis to identify molecular subtypes of GC. Characteristics and prognosis differences were analyzed among these molecular subtypes, and modular key genes in molecular subtypes were identified based on changes in expression correlation. Multivariate Cox proportional hazard analysis was used to find the independent prognostic gene. Kaplan-Meier method and log-rank test was used to estimate correlations of key DNA repair genes with GC patients'overall survival. There were 57 key genes significantly associated to GC patients' prognosis, and patients were stratified into three molecular clusters based on their expression profiles, in which patients in Cluster 3 showed the best survival (P < 0.05). After a three-phase training, test and validation process, the expression profile of 13 independent key DNA repair genes were identified can classify the prognostic risk of patients. Compared with patients with low-risk score, patients with high risk score in the training set had shorter overall survival (P < 0.0001). Furthermore, we verified equivalent findings by these key DNA repair genes in the test set (P < 0.0001) and the independent validation set (P = 0.0024). Our results suggest a great potential for the use of DNA repair gene profiling as a powerful marker in prognostication and inform treatment decisions for GC patients.