Comprehensive analysis of mitochondria-associated genes in glioblastoma via single-cell and bulk RNA sequencing

Glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor survival. Mitochondrial dysfunction and metabolic reprogramming, particularly the Warburg effect, are increasingly recognized as critical drivers of GBM progression. Here, we integrated single-cell (scRNA-seq) and bulk RNA-sequencing (bulk RNA-seq) data to comprehensively examine mitochondria-associated genes in GBM. We identified differentially expressed mitochondrial genes with prognostic significance and constructed a 9-gene risk signature—ACOT7, THEM5, MTHFD2, ABCB7, PICK1, PDK3, ARMCX6, GSTK1, and SSBP1—using LASSO and Cox regression. This signature robustly stratified patients into high- and low-risk groups in both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts, remaining an independent prognostic factor in multivariate analyses. Time-dependent ROC AUCs were 0.729, 0.813, and 0.828 at 1, 2, and 3 years in TCGA, and 0.597, 0.650, and 0.546 in CGGA. A nomogram integrating the signature with clinical variables achieved AUCs of 0.649, 0.820, and 0.854 at 1, 3, and 5 years, with good 1/2/3-year calibration. Functional enrichment and clustering analyses revealed distinct metabolic phenotypes and survival differences between subtypes. Single-cell pseudotime analysis showed a transition from oxidative phosphorylation to glycolysis in malignant cells, aligning with the Warburg effect and implicating metabolic reprogramming in immune modulation. Our findings underscore the prognostic value of mitochondria-associated genes and suggest potential therapeutic targets for disrupting GBM metabolism. Overall, these results establish a mitochondria-centric prognostic model supported by single-cell context; findings are hypothesis-generating and warrant prospective and experimental validation.