Construction and Validation of a Prognostic Prediction Model for Gastric Cancer Using a Series of Genes Related to Lactate Metabolism

Abstract

\n Background: Gastric cancer is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. The commonly used TNM staging and some common biomarkers have a certain value in predicting the prognosis of GC patients, but they gradually failed 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 as TCGA-STAD entire cohort, including TCGA-STAD training cohort (n=176) and TCGA-STAD testing cohort (n=174). GSE15459 (n=191), GSE62254 (n=300) and some cases in our center (n=12) were for external validation.Results: Through differential expression analysis and univariate Cox regression analysis in TCGA-STAD training cohort, we screened out 5 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 5 genes related to lactate metabolism to develop a prognostic prediction model for GC patients based on them. The prediction performance of the model is confirmed by a series of bioinformatics and statistical analysis.