Mitochondrial permeability transition drives the expression, identification and validation of necrosis-related genes in prognostic risk models of hepatocellular carcinoma.

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 (, , , , , and ) that were significantly associated with overall survival (OS) in patients. Based on the median risk score, we categorized HCC patients into high-risk and low-risk groups. Kaplan-Meier (KM) survival analysis results demonstrated a significant difference in OS between the two groups, which was further validated through additional assessment. Furthermore, we constructed a nomogram to predict the survival probability of HCC patients. Moreover, GSEA revealed a crucial correlation between these genes and HCC, and highlighted a close association between risk scores and regulatory T cells. We also identified four chemotherapy drugs related to HCC. Finally, in both the training and validation cohorts, , , and exhibited high expression levels in tumor samples. Further validation using RT-qPCR confirmed that the expression of all prognostic genes was significantly higher in HCC group compared to the control group.

CONCLUSIONS

This study explored six prognostic genes (, , , , and ) associated with MPT-DNRGs in HCC, which provides a reference for further research on HCC.