Predictive and prognostic value of a glucose metabolism disorder and immune-related gene signature in glioma.

Glioma is a highly malignant intracranial tumor with poor prognosis and inevitable recurrence. Glycolysis-targeted therapy has preclinical potential for glioma but limited clinical application, partly due to unclear crosstalk between glucose metabolism and the tumor immune microenvironment (TME). This study aimed to construct a glucose metabolism-immune-related gene signature for glioma, validate its prognostic value, and explore underlying mechanisms. Multi-omics data from TCGA, GEO (GSE16011,GSE15824), and CGGA were integrated. Glucose metabolism-related genes (GRGs) and immune-related genes (IRGs) from GeneCards were intersected with differentially expressed genes (DEGs) between glioma and normal tissues to get 64 G&IRDEGs. Univariate Cox and LASSO regression screened 16 key genes to establish the GIRPG prognostic model. Patients were stratified into high/low-risk groups by median GIRPG score. High-risk patients had significantly worse overall survival (OS) in both TCGA (HR = 7.25, 95% CI:5.36-9.80, P < 0.001) and CGGA (P < 0.001) cohorts. Time-dependent ROC analysis confirmed robust predictive accuracy, with 1-/3-/5-year OS AUCs of 0.884/0.908/0.848 (TCGA) and 0.782/0.805/0.793 (CGGA). GIRPG was identified as an independent prognostic factor (HR = 2.509, 95%CI:2.035-3.093, P < 0.001), and a nomogram integrating GIRPG with age and histological grade achieved the highest 5-year OS predictive accuracy. Functional analyses linked GIRPG to glucose metabolism-immunity crosstalk pathways (IL-10/IL-17 synthesis, PD-1 signaling), while PPI and single-cell analyses identified hub genes (MAP3K1, CD44, IL10) and cell-type-specific expression patterns. GIRPG independently predicts glioma outcomes, reflects glucose metabolism-immunity crosstalk in the TME, and provides a prognostic tool and potential therapeutic targets for glioma precision treatment.