A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

Abstract

Abstract Background Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients. Methods The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log‐rank test and a Cox proportional hazards regression model. Results The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value − 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan–Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345–7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients. Conclusion Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.