Lipid Metabolism related Gene Expression is correlated with Immune-metabolic Microenvironment and Predicts Prognosis in Hepatocellular Carcinoma

Abstract

Abstract Background & Aims Hepatocellular Carcinoma (HCC)is a leading cause of cancer mortality worldwide. This study was aimed at exploring the prognosis predictive ability of lipid metabolism related genes (LMRGs) in HCC and constructing a reliable risk model for clinical management. Methods Bioinformatics analysis of transcription data obtained from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the International Cancer Genome Consortium (ICGC) database was utilized in this study. COX regression and consensus clustering were performed to identify two molecular subgroups based on LMRGs. Immune infiltrating analysis, KEGG, GSVA, and GO pathway analysis was applied to clarify the underlying mechanisms of LMRGs participated in the prognosis of HCC. We then performed LASSO-COX regression analysis to build the risk model and validate the model in an external HCC cohort from the ICGC database. Results We identified two molecular subgroups with distinct overall survival based on the different expression profiles of LMRGs. The increased immune score and expression of immune checkpoints, altered immune cell subtypes, and dysregulated metabolic pathways were involved in the worse overall survival of the molecular subgroup with higher expression of the majority of LMRGs. The risk model based on four LMRGs including SRD5A3, PPARGC1A, HSD17B12, AKR1B15 and the integrated nomogram established with the four LMRGs and TNM stage performed reliable predictive ability of HCC prognosis both in the train set and validation set. Conclusion LMRG dysregulation is correlated with the immune microenvironment and various metabolic pathways in HCC. The risk model developed based on the expression of LMRGs could act as a potent predictor of HCC prognosis.