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Background: Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. The commonly used tumor-node-metastasis (TNM) staging and some common biomarkers have a certain value in predicting the prognosis of GC patients, but they gradually fail to meet the clinical demands. Therefore, we aim to construct a prognostic prediction model for GC patients., Methods: A total of 350 cases were included in the STAD (Stomach adenocarcinoma) entire cohort of TCGA (The Cancer Genome Atlas), including the STAD training cohort of TCGA (n = 176) and the STAD testing cohort of TCGA (n = 174). GSE15459 (n = 191), and GSE62254 (n = 300) were for external validation., Results: Through differential expression analysis and univariate Cox regression analysis in the STAD training cohort of TCGA, we screened out five genes among 600 genes related to lactate metabolism for the construction of our prognostic prediction model. The internal and external validations showed the same result, that is, patients with higher risk score were associated with poor prognosis (all p  < 0.05), and our model works well without regard of patients' age, gender, tumor grade, clinical stage or TNM stage, which supports the availability, validity and stability of our model. Gene function analysis, tumor-infiltrating immune cells analysis, tumor microenvironment analysis and clinical treatment exploration were performed to improve the practicability of the model, and hope to provide a new basis for more in-depth study of the molecular mechanism for GC and for clinicians to formulate more reasonable and individualized treatment plans., Conclusions: We screened out and used five genes related to lactate metabolism to develop a prognostic prediction model for GC patients. The prediction performance of the model is confirmed by a series of bioinformatics and statistical analysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors. Published by Elsevier Ltd.)