To investigate the prognostic value of fatty acid metabolism-related genes in glioma, construct a comprehensive model based on these genes, and analyze its associations with the immune microenvironment and drug sensitivity.

This study integrated transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and the Genotype Tissue Expression (GTEx) databases. Differentially expressed genes and Cox proportional hazards model regression were first used to screen for core prognostic fatty acid metabolism-related genes. Glioma patients were then randomly divided into a training set and a validation set at a 7:3 ratio. A risk score model was constructed using the random forest algorithm. Survival analysis was performed on age, gender, and race to identify survival-related factors, which were then combined with methylation site levels to develop a comprehensive nomogram. Consensus clustering was subsequently applied to classify glioma samples into two fatty acid metabolism subtypes (subtype 1/low-metabolism group and subtype 2/high-metabolism group). Immune infiltration characteristics and drug sensitivity were evaluated using the CIBERSORT algorithm and the Genomics of Drug Sensitivity in Cancer database. Finally, expression levels of the core genes were validated in glioma cells by real-time quantitative polymerase chain reaction (RT-qPCR).

Four core fatty acid metabolism-related genes were identified: ACOT2, ACOT4, ACLS5, and ADH6. Based on these four genes, a random-forest risk model was constructed, and glioma patients were randomly split into a training set (422 patients) and a validation set (182 patients) in a 7:3 ratio. The model exhibited robust performance in both datasets, with areas under the curve (AUCs) for predicting 1-, 3-, and 5-year survival all greater than 0.75 in the training set and all greater than 0.6 in the validation set. The combined nomogram also exhibited excellent predictive accuracy (calibration curve R²=0.92). Immune infiltration analysis revealed significant enrichment of M2 macrophages in glioma tissues. Drug sensitivity analysis indicated that subtype 2 (high-metabolism group) was more sensitive to agents such as Fluorouracil (with half-maximal inhibitory concentration reduced by 30%-50%). Cell experiments confirmed that the above core genes were significantly overexpressed in glioma (P<0.001).

The comprehensive model based on fatty acid metabolism-related genes (ACOT2, ACOT4, ACSL5, ADH6), MGMT methylation status, and patient age can effectively stratify the prognosis of glioma, providing a new theoretical basis for individualized treatment strategies.