Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy.

Background: Targeting lactate metabolism represents a promising therapeutic strategy to enhance anti-tumor immune responses. In this study, we developed a novel model based on lactate metabolism-related genes (LRGs) to predict survival, characterize the immune microenvironment, and assess immunotherapy response in gastric cancer (GC), with the potential to identify new biomarkers.

Methods: Data sets of GC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. LRGs were sourced from the MSigDB database. Five key prognostic LRGs (MMP11, MMP12, HBB, VSIG2, and SERPINE1) were identified using univariate COX regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Patients were classified into high-risk and low-risk groups based on a median risk score. We conducted prognostic analysis, gene set enrichment analysis (GSEA), immune microenvironment analysis, immunotherapy responsiveness evaluation, and drug screening in these groups.

Results: The high-risk group exhibited poorer prognosis compared to the low-risk group, as predicted by our nomogram for overall survival. Notably, the high-risk group, marked by higher stromal cell infiltration and RNA stemness scores (RNAss), showed increased susceptibility to immune evasion. In contrast, the low-risk group demonstrated better responses to immunotherapy and greater sensitivity to chemotherapy. Single-cell analysis revealed that SERPINE1 is predominantly positively correlated with immune checkpoint expression, while VSIG2 exhibits a negative correlation.

Conclusions: We have developed and validated a novel lactate metabolism-associated model, providing new insights into the prognosis and immunotherapy of GC patients. The five identified LRGs offer potential as prognostic biomarkers and therapeutic targets in GC.