Background Gastric cancer (GC) is one of the most common tumors and has a poor prognosis. Many studies have shown that aging is closely related to the occurrence and development of cancer. This paper aims to construct an aging gene-related prognostic risk model for early diagnosis and prognosis prediction of gastric cancer.Methods Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO), and aging-related genes (ARGs) were obtained from Human 3 Aging Genomic Resources. The prognostic model was constructed using the GEO database as the training set, and the TCGA database was used as the validation set to validate the prognostic model. To analyze the predictive value and potential clinical application of ARGs in gastric cancer.Results we analyzed ARGs expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO). After LASSO and Cox regression analysis, 5 ARG-based features (A2M, AGRT1, CTF1, SNCG, and SERPINE1) were constructed using the GEO dataset to significantly classify cases into high- and low-risk groups according to overall survival (OS). Cox regression analysis showed that ARG features were independent prognostic factors for GC. A nomogram based on ARG characteristics and clinicopathological factors was developed in the GSE62254 (ACRG) cohort and validated in the TCGA dataset. Calibration plots show good agreement between the nomogram's predictions and actual observations. Receiver decision curve analysis showed that ARG nomogram had better OS prediction and clinical net benefit than staging system.Conclusions We developed and validated five aging-related genes prognostic models for gastric cancer, including A2M, AGRT1, CTF1, SNCG, and SERPINE1. This model can be used as a new molecular marker for prognosis prediction of gastric cancer,