A lipid metabolism–based prognostic risk model for HBV–related hepatocellular carcinoma

Abstract

Abstract Background Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV–related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV–related HCC. This study established a prognostic model to stratify HBV–related HCC based on lipid metabolisms. Methods Based on The Cancer Genome Atlas HBV–related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis. Results The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low–risk groups harbored longer survival times (with P < 0.05, log–rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high–risk groups. Low–risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid–metabolism (including “Fatty acid metabolism”) and immune pathways were significantly enriched (P < 0.05) in the low–risk groups. Conclusions This study established a robust model to stratify HBV–related HCC effectively. Analysis results decode in part the heterogeneity of HBV–related liver cancer and highlight perturbation of lipid metabolism in HBV–related HCC. This study’s findings could facilitate patients’ clinical classification and give hints for treatment selection.