Prognostic and therapeutic response prediction in glioblastoma multiforme using a lactylation-associated gene...

Glioblastoma (GBM) is a highly aggressive brain tumor characterized by immunosuppressive tumor microenvironment and metabolic reprogramming. Dysregulated lactate metabolism has been observed in the glioma microenvironment, yet the functional roles of lactate accumulation and lactylation modification in driving GBM progression remain poorly understood.

We identified core genes associated with lactylation by analyzing gene expression data from GBM tumor tissues and normal tissues, combined with the screening of lactylation-related genes. Utilizing LASSO regression analysis, we developed an optimized five-gene lactylation-related signature, validated within the CGGA cohort. Patients were categorized into high- and low- LRGS groups based on risk scores. Subsequently, we compared clinical outcomes, immune profiles, and therapeutic sensitivities between these two groups. To further elucidate the impact of lactylation on immune cells, we analyzed single-cell sequencing data from the tumor microenvironment of GBM patients.

We identified five lactylation-related hub genes strongly associated with GBM and constructed a lactylation-related gene prognostic model. The risk scores of this lactylation-related model showed strong correlations with immune cell infiltration levels. Single-cell sequencing analysis revealed that significant differences in lactylation levels across various immune cell subtypes, with tumor-associated fibroblasts exhibiting markedly higher lactylation activity. Additionally, patients in the low- LRGS group demonstrated enhanced sensitivity to chemotherapy and targeted therapies.

The lactylation-related gene signature established in this study demonstrates robust predictive efficacy in glioblastoma, serving not only as a prognostic biomarker but also offering new directions for personalized treatment strategies.