Lactylation-related gene signature accurately predicts prognosis and immunotherapy response in gastric cancer.

Background: Gastric cancer (GC) is a malignant tumor associated with significant rates of morbidity and mortality. Hence, developing efficient predictive models and directing clinical interventions in GC is crucial. Lactylation of proteins is detected in gastric cancer tumors and is linked to the advancement of gastric cancer. Methods: The The Cancer Genome Atlas (TCGA) was utilized to analyze the gene expression levels associated with lactylation. A genetic pattern linked to lactylation was created using Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The predictive ability of the model was evaluated and confirmed in the Gene Expression Omnibus (GEO) cohort, where patients were divided into two risk groups based on their scores. The study examined the relationship between gene expression and the presence of immune cells in the context of immunotherapy treatment. Results: 29 predictive lactylation-related genes with differential expression were discovered. A signature consisting of three genes was developed and confirmed. Patients who had higher risk scores experienced worse clinical results. The group with lower risk showed increased Tumor Immune Dysfunction and Exclusion (TIDE) score and greater responsiveness to immunotherapy. The tumor tissues secrete more lactate acid than normal tissues and express more PD-L1 than normal tissues, that is, lactate acid promotes the immune evasion of tumor cells. In GC, the lactylation-related signature showed strong predictive accuracy. Utilizing both anti-lactylation and anti-PD-L1 may prove to be an effective approach for treating GC in clinical settings. We further proved that one of the lactate metabolism related genes, SCL16A7 could promote the expression of PD-L1 in GC cells. Conclusion: The risk model not only provides a basis for better prognosis in GC patients, but also is a potential prognostic indicator to distinguish the molecular and immune characteristics, and the response from Immune checkpoint inhibitors (ICI) therapy and chemotherapy in GC.