Lactate Metabolism‐Immune Regulation‐Related Gene Signature in Lower‐Grade Gliomas: Prognostic Model Development and Immune Characterization

Low-grade gliomas represent a diverse category of central nervous system tumors, characterized by considerable variability in prognosis. Metabolic reprogramming, particularly lactate metabolism, has been associated with the tumor immune microenvironment; however, the prognostic significance of the related genes in LGG is still not well understood. This study used TCGA and CGGA cohorts to perform consensus clustering based on genes involved in lactate metabolism. A predictive model was constructed using differential analysis, Cox regression, and LASSO methods. The model's association with immune cell infiltration, TMB, and drug responsiveness was also evaluated. The function of the key gene PLA2G2A was validated using RT-qPCR, Western blot, immunofluorescence, ELISA, CCK-8, Transwell assays, and mouse tumorigenesis experiments. Consensus clustering classified LGG into two distinct subtypes, each showing significant variations in survival and immune profiles. The three-gene signature (VAV3, TNFRSF12A, PLA2G2A) effectively distinguished high-risk from low-risk patients, demonstrating high predictive accuracy in both TCGA and CGGA datasets. Patients categorized as high-risk showed reduced immune infiltration, increased TMB, and a worse prognosis. Additionally, they showed increased sensitivity to tyrosine kinase inhibitors and platinum-based therapies. Functional experiments showed that PLA2G2A was highly expressed in gliomas, and its downregulation notably reduced tumor cell proliferation, migration, invasion, and growth in vivo, while reversing the epithelial-mesenchymal transition. This study proposes and validates a novel three-gene prognostic model that reflects the metabolic-immune interactions in LGG, predicts patient prognosis, and suggests potential therapeutic targets. The functional validation of PLA2G2A further underscores its potential as a therapeutic biomarker.