Glioblastoma (GBM) remains a lethal brain tumor with limited prognostic tools. Metabolic reprogramming, particularly in understudied pathways like propionate metabolism, may offer new biomarkers. Here, we identified a novel prognostic signature based on seven propionate metabolism‐related genes (SLC9A1, ELANE, ACADS, SOAT2, MYD88, ADSL, and BMP2) from the TCGA‐GBM cohort. A risk scoring model was constructed via LASSO Cox regression effectively stratified patients into high‐ and low‐risk groups with significant survival differences, which was also validated in independent GEO datasets. Multiomics analysis revealed that the high‐risk group was associated with an immunosuppressive microenvironment, characterized by increased immune checkpoint expression and distinct immune cell infiltration. Mutational profiling showed a strong association with key driver alterations, including enrichment of RB1 mutations in high‐risk and IDH1 mutations in low‐risk patients. Single‐cell RNA‐seq (scRNA‐seq) analysis confirmed the specific enrichment of signature genes within malignant cells, and coexpression network analysis (hdWGCNA) further linked the high‐risk phenotype to transcriptional modules. In conclusion, we established and validated a robust metabolic gene signature that not only predicts prognosis but also delineates a high‐risk GBM subtype defined by integrated metabolic, immunogenomic, and transcriptional features, providing new insights into the determinants of GBM aggressiveness.
Li et al. (Thu,) studied this question.