Objective In this study, we examined the role of lactate in gastric cancer (GC) and its prognostic significance.

Methods Data obtained for gastric cancer patient were retrieved from The Cancer Genome Atlas (TCGA) database, and information pertaining to lactylation-related genes (LRGs) was obtained by integrating data from the Gene Set Enrichment Analysis (GSEA) database and relevant literature. LRGs that are differentially expressed in GC were identified, and aunivariate Cox regression analysis was performed to identify those associated with the prognosis of GC patients. In addition, Lasso regression was applied to facilitate construction of a lactic acidosis-related prognostic risk model, whereas survival and receiver operating characteristic curve analyses were used to evaluate the predictive performance of the model. By integrating the prognostic model with clinicopathological characteristics, we also developed a nomogram and assessed the correlations of this prognostic model with the tumor microenvironment, immune cell infiltration, and drug sensitivity. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical staining analyses were conducted to verify differences in the expression of prognostic genes in GC and adjacent normal tissues.

Results We identified fifteen prognosis-related differentially expressed lactylation-related genes (LR-DEGs), and successfully constructed a prognostic risk model based on nine of these genes, which showed excellent performance in predicting the overall survival of GC patients (P< 0.01). Significant differences were also detected between high- and low-risk groups with respect to the tumor microenvironment, immune cell infiltration, and drug sensitivity (P<0.05). Furthermore, compared with normal tissues, the results of qRT-PCR analysis and immunohistochemical staining revealed the upregulated expression of SPP1 and SLC5A12 in GC tissues (P<0.05).

Conclusions We anticipate that the LRGs prognostic risk model established in this study will serve as a reliable tool for predicting the prognosis of gastric cancer patients and provide novel insights for the development of personalizedclinical treatment strategies.