ArticleDiscover oncology2025
Identification of mitophagy-related genes impacting patient survival in glioma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrating of Machine Learning to Characterize Tumor-Infiltrating Immune Cell-Related Signature for Enhancing Prognosis for Glioma Patients.Biological procedures online · 2025Article
- Targeting the Mitochondria in High-Grade Gliomas.Cancers · 2025Review
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2 authors.
Funding
Abstract
backgroundThis study presents a new prognostic model using mitophagy-related genes (MRGs) in glioma, a type of brain tumor, developed through bioinformatics. The model seeks to improve the understanding of glioma prognosis by focusing on mitophagy, a cellular process that eliminates damaged mitochondria and influences tumor behavior and patient outcomes.
methodsThe expression profile and clinical information of patients were downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus). By analyzing the correlation between the 14 MRGs and glioma prognosis, we established a novel prognostic model in the TCGA training cohort and validated it in the GSE16011 dataset.
resultsUsing univariate Cox regression, we identified 26 MRGs that were significantly enriched in various mitophagy-related pathways. After filtering variables using least absolute shrinkage and selection operator (Lasso) regression analysis, 14 MRGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). The risk score was found to be an independent prognostic factor for glioma in both univariate and multivariate Cox regression analyses. Interestingly, Geneset enrichment analysis (GSEA) analysis revealed that multiple signaling pathways related to neurotransmission were significantly enriched in the high-risk group. Additionally, a hub miRNA-mRNA network was established, which disclosed the quantity and classification of miRNAs capable of interacting with 14 MRGs. Finally, our analysis revealed a notable association between 14 MRGs and immune functionality in gliomas.
conclusionWe developed a robust and accurate prognostic model with 14 MRGs. Our findings might provide a reference for the clinical prognosis and management of glioma.
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