Mitochondrial cholesterol metabolism related gene model predicts prognosis and treatment response in hepatocellular carcinoma.

Abstract

Background: The persistently high mortality and morbidity rates of hepatocellular carcinoma (HCC) remain a global concern. Notably, the disruptions in mitochondrial cholesterol metabolism (MCM) play a pivotal role in the progression and development of HCC, underscoring the significance of this metabolic pathway in the disease's etiology. The purpose of this research was to investigate genes associated with MCM and develop a model for predicting the prognostic features of patients with HCC.

Methods: MCM-related genes (MCMGs) were identified through The Cancer Genome Atlas (TCGA), The Molecular Signatures Database (MsigDB), and the Mitocarta3.0 databases. Differential gene expression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed using R software to construct a MCM-related model. This model underwent further analysis for somatic mutations, single sample gene set enrichment analysis (ssGSEA), stromal and immune cell estimation, immune checkpoint evaluation, and drug susceptibility prediction to assess the tumor microenvironment (TME) and therapeutic responses. The mRNA expression levels of the genes associated with the model were quantified using real-time fluorescence quantitative polymerase chain reaction (RT-qPCR).

Results: The model, which included six MCMGs (ACADL, ACLY, TXNRD1, DTYMK, ACAT1, and FLAD1), divided all patients (age ≤65 vs. >65 years, P<0.001; male vs. female, ns) into a high-risk group and a low-risk group. The high-risk group showed a higher mortality rate and lower survival rate with AUC of 0.785, 0.752, 0.756, 0.774 and 0.759 for the 1-, 2-, 3-, 4-, and 5-year respectively. A nomogram based on risk score, stage, T, and M had a better prognostic accuracy, with AUC of 0.808, 0.796, 0.811, 0.824 and 0.795 for the 1-, 2-, 3-, 4-, and 5-year respectively. The high-risk group showed enrichment in cell cycle, cell division, and chromosome processes, and a significantly higher tumor mutation burden (TMB) value compared to the low-risk group. Further immune infiltration analysis indicated a significantly reduction in the abundances of some immune cells (activated CD4 T cells, type 2 helper T cells, and neutrophils) and significantly higher expression levels of some immune checkpoint (CD80, CTLA4, HAVCR2, and TNFRSF4) in the high-risk group. Moreover, the risk score was associated with the response to immune checkpoint inhibitors (ICIs) therapy and efficiencies of multiple chemotherapy drugs.

Conclusions: This study developed a prognostic model based on MCMGs, which can predict the prognosis of liver cancer patients and their response to immunotherapy and chemotherapy. The model may provide new strategies to enhance the prognosis and treatment of HCC.