Background: Gastric cancer has substantial biological differences between Asian and non-Asian populations, which makes it difficult to have a unified predictive measure for all people. We aimed to identify novel prognostic biomarkers to help predict the prognosis of patients with GC in Asia. Methods: We investigated the differential gene expression between GC and normal tissues of GSE66229. Univariate, multivariate and Lasso Cox regression analyses were conducted to establish a four-gene-related prognostic model. Validation of the prognostic model was conducted using The Cancer Genome Atlas (TCGA) database. Finally, we established a nomogram containing clinical characteristics and the prognostic model and validated it in the GEO database Findings: A total of 252 differentially expressed genes (DEGs) were obtained. Four genes (RBPMS2, RGN, PLEKHS1, CT83) were selected to establish the prognostic model, and the patients were classified into a low-risk group and a high-risk group. The independent prognostic ability with other clinical characteristics was indicated by multivariate analysis. The Kaplan-Meier curves of the two risk groups were significantly different. ROC analysis confirmed the sensitivity and specificity of the prognostic model. Next, the prognostic model was validated in the TCGA global cohort and the TCGA Asian cohort. Ultimately, the concordance index (C-index) of the nomogram for evaluating the OS was 0.76, 12% higher than that only focus on the pathologic stage. Interpretation: The four-gene-related prognostic model and the nomogram are reliable tools for predicting the overall survival (OS) of Asian patients. The predictive power is limited in the non-Asian population. Funding Statement: This work was supported by National Key Technology Support Program (No.2014BA109B02), the Beijing Municipal Science and Technology Project (No. D131100005313010), National Science Foundation for Young Scientists of China (81802735) and the grants from ‘San Ming’ Project of Shenzhen city, China(No.SZSM201612051). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: All the obtained data were used according to the GEO and TCGA data access policies, as well as publication guidelines. Therefore, the study does not need to be approved by the local ethics committee.