Abstract
BACKGROUND: As one of the most common malignant tumors worldwide, the morbidity and mortality of gastric carcinoma (GC) are gradually increasing. The aim of this study was to construct a signature according to immune-relevant genes to predict the survival outcome of GC patients using The Cancer Genome Altas (TCGA). METHODS: Univariate Cox regression analysis was used to assess the relationship between immune-relevant genes regarding the prognosis of patients with GC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC. Multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to assess the independent prognostic ability of the immune-relevant gene signature. RESULTS: A total of 113 prognostic immune-relevant genes were identified using univariate Cox proportional hazards regression analysis. A signature of nine immune-relevant genes was constructed using the LASSO Cox regression. The GC samples were assigned to two groups (low- and high risk) according to the optimal cutoff value of the signature score. Compared with the patients in the high-risk group, patients in the low-risk group had a significantly better prognosis in the TCGA and GSE84437 cohorts (log-rank test P < 0.001). Multivariate Cox regression analysis demonstrated that the signature of nine immune-relevant genes might serve as an independent predictor of GC. CONCLUSIONS: Our results showed that the signature of nine immune-relevant genes may potentially serve as a prognostic prediction for patients with GC, which may contribute to the decision-making of personalized treatment for the patients.