A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma

Abstract

Globally, hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Studies have shown that alterations in the tumor immune microenvironment (TIME) play a significant role in the pathogenesis and progression of HCC, and notably, lipid metabolism has been shown to regulate TIME. Therefore, in predicting the prognosis and efficacy of immunotherapy in patients with HCC, lipid metabolism-related prognostic factors are highly relevant. mRNA expression data of HCC were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and gene expression omnibus (GEO) databases. and lipid metabolism-related genes were also obtained from the GSEA databases. Least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox proportional hazards analysis were used to explore lipid metabolism-related prognostic genes and further construct a prognostic signature in the training set, ICGC and GSE54236 were used to validate the accuracy of the signature. qRT-PCR was used to detect the mRNA levels of lipid metabolism-related prognostic genes in HCC tissues and their paired adjacent tissues. Nile red staining was used to demonstrate lipid content in HCC tissues. Immunofluores-cence and ELISA were used to detect immune cells and immune responses in HCC tissues and serum. Six lipid metabolism-related genes (ADH1C, APEX1, ME1, S100A10, ACACA and CYP2C9) were identified as independent prognostic factors, which were used for risk model construction for HCC patients. The areas under the 1-, 2-, and 3-year ROC curves for the TCGA cohort were 0.758, 0.701 and 0.671, respectively. Compared with paired paracancerous tissues, qRT-PCR revealed that APEX1, ME1, S100A10 and ACACA were up-regulated in HCC tissues, whereas ADH1C and CYP2C9 were down-regulated in HCC tissues. Nile red staining indicated that this study showed that both the HCC tissue and serum of patients in the high-risk group exhibited lipid accumulation. Our identified prognostic model comprising six lipid metabolism-related genes could provide survival prediction. Moreover, HCC drug therapy target selection and molecular marker research can be guided by our predictive model.