Comprehensive Analysis of Mitochondria-Associated Genes in Glioblastoma via Single-Cell and Bulk RNA Sequencing
Authored by Fangyuan Wang, Yupei Guo, Chenglin Zhao, Zhou Jing, Qingyi Wang, Minghe Li, Bingqi Lu, Ao Huo, Hao Li, Wulong Liang, Weihua Hu, and Xudong Fu
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Abstract
Background/Objectives Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis and limited treatment options. Metabolic reprogramming, particularly the Warburg effect, plays a significant role in its progression. This study aims to explore the prognostic value and therapeutic potential of mitochondria-associated genes in GBM by integrating single-cell and bulk RNA sequencing data. Methods We analyzed single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and bulk RNA sequencing (bulk RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Differentially expressed mi-tochondria-associated genes were identified, and a 9-gene risk signature was con-structed using LASSO and Cox regression models. Survival analysis and functional en-richment were conducted, and single-cell pseudotime analysis was employed to inves-tigate metabolic transitions. Results We identified 10 mitochondria-associated genes significantly correlated with GBM prognosis. A 9-gene risk signature was developed and validated, stratifying patients into high- and low-risk groups in both the TCGA and CGGA cohorts. This model was shown to be an independent prognostic factor. Functional enrichment revealed associations with metabolic pathways, including the Warburg effect. Single-cell analysis highlighted a shift from oxidative phosphorylation to glycolysis in malignant cells, further implicating metabolic reprogramming in GBM progression. Conclusions Our findings emphasize the prognostic value of mitochon-dria-associated genes in GBM and their potential as therapeutic targets. The developed risk model can aid in prognostic evaluation and personalized treatment strategies, particularly by targeting the metabolic reprogramming that drives tumor progression. These results provide a new avenue for improving GBM management through meta-bolic interventions.
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