ArticleTranslational cancer research2025
Mitochondrial permeability transition drives the expression, identification and validation of necrosis-related genes in prognostic risk models of hepatocellular carcinoma.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel prognostic signature based on mitochondrial permeability transition-driven necrosis genes for biochemical recurrence prediction in prostate cancer.Frontiers in oncology · 2026Article
- Prognostic value of a simplified score based on routine parameters in patients with hepatocellular carcinoma treated with systemic therapies: a retrospective cohort study.Journal of gastrointestinal oncology · 2025Article
- PANoptosis-associated genes exhibit significant potential in the diagnosis of hepatocellular carcinoma.Journal of gastrointestinal oncology · 2025Article
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6 authors.
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Abstract
Background: Hepatocellular carcinoma (HCC) is a prevalent malignant tumor, and the current treatment methods exhibit various limitations. In recent years, the role of mitochondrial permeability transition-driven necrosis-related genes (MPT-DNRGs) in the pathogenesis and progression of severe diseases, particularly tumors, has garnered significant attention. This study aimed to identify new targets and concepts for MPT-DNRG-targeted therapy in HCC. Methods: In this study, we utilized HCC-related datasets and MPT-DNRGs to identify differentially expressed genes (DEGs) between HCC patients and control groups. By conducting a cross-analysis of the results of DEGs and MPT-DNRGs, we screened candidate genes. Subsequently, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis methods were employed to identify prognostic genes, which were used to construct a risk model and calculate individual risk scores for HCC patients. Additionally, we performed univariate and multivariate Cox regression analyses to identify independent prognostic factors and constructed a column chart based on these factors to predict the survival probability of HCC patients. Furthermore, gene set enrichment analysis (GSEA), the immune microenvironment, chemotherapy drugs, and the expression of prognostic genes between the two groups were analyzed. Finally, the expression of these prognostic genes was further confirmed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology. Results: In this study, we identified 8,515 DEGs between HCC and control samples. By performing intersection analysis between DEGs and MPT-DNRGs, we pinpointed 15 candidate genes. Subsequently, through univariate Cox regression and LASSO regression analysis, we identified six genes ( Conclusions: This study explored six prognostic genes (
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