ArticleTranslational cancer research2025
Investigation of mitochondrial DNA methylation-related prognostic biomarkers in hepatocellular carcinoma using The Cancer Genome Atlas (TCGA) database.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Targeting the E2F6-TOP2A-DKK1 axis: a novel therapeutic strategy for EMT-driven hepatocellular carcinoma progression.Frontiers in immunology · 2026Article
- Targeting Metabolism in Cancer Therapy: Inhibitors and Approaches.Cancer treatment and research · 2026Review
- Identification of basement membrane-based prognostic signature and potential therapeutic drugs in hepatocellular carcinoma.Translational cancer research · 2025Article
- Bioinformatics identification of key microRNA-correlated genes associated with hepatocellular carcinoma heterogeneity and prognosis.BMC gastroenterology · 2025Article
- Mitochondrial epigenetic mechanisms in cancer: an updated overview.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally, with complex pathogenesis and limited therapeutic options. Emerging evidence suggests that mitochondrial DNA methylation (MTDM) plays a regulatory role in tumorigenesis, but its specific contributions to HCC progression, prognosis, and tumor microenvironment (TME) remodeling remain poorly characterized. This study aims to investigate MTDM-associated molecular subtypes in HCC, screen potential prognostic biomarkers linked to MTDM dysregulation, and explore their implications for immune landscape heterogeneity and therapeutic response. Methods: Several HCC datasets and MTDM-related prognostic genes associated with the clinicopathological features of HCC were collected from public databases. The ConsensusClusterPlus tool was used for unsupervised clustering to identify the MTDM differentially expressed genes (DEGs) and then the candidate genes. Subsequently, a univariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis were performed on the data of the candidate genes to identify and validate the prognostic genes. Additionally, differences in the TME and the enriched pathways between the high- and low-risk groups were evaluated, and drug response prediction was performed using the pRRophetic R package. Results: Eight MTDM-related genes were found to be differentially expressed in HCC. In relation to these MTDM-related DEGs, two molecular subtypes of HCC (Cluster 1 and Cluster 2) were identified. In addition, 333 candidate genes were identified. The regression analysis of the DEGs included in the risk model identified Conclusions: This study constructed a risk model for HCC based on two identified prognostic genes (
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