BACKGROUND: The aim of the study was to identify a multigene prognostic factor in patients with gastric cancer (GC).

MATERIAL AND METHODS: Random survival forest (RSF) was performed to screen survival-related genes and develop a multigene combination based on the cumulative hazard function of each GC patient in TCGA-STAD and GSE15459. Kaplan-Meier curve and univariate and multivariable Cox proportional hazards regression model were applied to evaluate the prognostic performance of the 5-gene combination. C-index was used to compare the prognostic performance of the 5-gene combination and another 9-gene signature in GC. Gene set enrichment analysis (GSEA) was conducted.

RESULTS: We obtained 19 survival-related genes through univariate Cox proportional hazards analysis in the training set, 5 of which were identified and were used to develop a 5-gene combination through RSF. Patients in the 5-gene combination low-risk group had better overall survival (OS) than those in the 5-gene combination high-risk group, and the 5-gene combination was demonstrated to be an independent prognostic factor in patients with GC. The 5-gene combination outperformed the 9-gene signature in predicting the OS of GC patients, and it might affect the prognosis of GC patients through E2F signaling, MYC signaling, and G2M checkpoint.

CONCLUSIONS: We introduce a 5-gene combination that can predict the survival of GC patients and might be an independent prognostic factor in GC.

Keywords: Aged, 80 and over, Biomarkers, Tumor, Databases, Genetic, Gene Expression Profiling, Proportional Hazards Models, Risk Factors