Investigation of mitochondrial DNA methylation-related prognostic biomarkers in hepatocellular carcinoma using The Cancer Genome Atlas (TCGA) database.

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 ADH4 and DNASE1L3 as prognostic genes that could be used to predict the overall survival of the HCC patients. The results of the differential immune recognition by immune cells using immune cell infiltration and the prognostic genes showed that the strongest negative correlation [correlation coefficient (r) =-0.312] was between ADH4 and activated cluster of differentiation (CD)4⁺ T cells, while the strongest positive correlation (r=0.332) was between DNASE1L3 and effector memory CD8⁺ T cells. The gene set enrichment analysis revealed five Kyoto Encyclopedia of Genes and Genomes pathways in the high- and low-risk groups that were clearly enriched in biological processes and signaling pathways, such as fatty acid degradation and peroxisome. The chemotherapeutic drug sensitivity analysis revealed significant differences in sensitivity to BI.2536 [a Polo-like kinase 1 (Plk1) inhibitor], A.443654 [a protein kinase B (Akt) 1/2 inhibitor], and ABT.888 [Veliparib, a poly(ADP-ribose) polymerase 1/2 (PARP1/2) inhibitor] between the high- and low-risk groups.

Conclusions: This study constructed a risk model for HCC based on two identified prognostic genes (ADH4 and DNASE1L3). It also elucidated the pathogenesis of MTDM-associated HCC. Our findings provide novel insights that could lead to the development of future therapeutic strategies.