No abstract

center of hepato-panreato-bliiay surgery the rst a liated hospital of Sun Yat-sen University Xiwen Wu center of hepato-pancreato-biliary surgery the rst a liated hospital of sun yat-sen university Shufen Liao center of surgery and anaesthiology the rst a liated hospital of sun yat-sen university shutong wang center of hepato-pancreato-biliary surgery the rst a liated hospital of sun yat-sen hospital shuirong lin center of hepato-pancreato-biliary surgery the rst a liated hospital of sun yat-sen university zimin song center of hepato-pancreato-biliary surgery the rst a liated hospital of sun yat-sen university baogang peng center of hepato-pancreato-biliary surgery the rst a liated hospital of sun yat-sen university Shunli Shen ( 

BackgroundLipid metabolism is important in tumor progression. However, its role in hepatocellular carcinoma (HCC) remains unknown. We attempt to build a lipid metabolism-related signature to evaluate its role in predicting the prognosis of HCC patients. MethodsWe obtained differential expression genes (DEGs) through differential analysis of mRNA expression between tumor tissues and paraneoplastic tissue of patients with HCC. The lipid metabolism-related genes were obtained from KEGG and MisDB. The corresponding gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Prognosis-related genes were obtained by COX regression analysis. Intersecting genes were defined as genes shared by DEGs and prognosis-related genes. The least absolute shrinkage and selection operator (LASSO) technique was used to calculate the prognostic genes and coefficients for forming a prognostic assessment signature. Kaplan–Meier survival analysis was applied to assess the model’s credibility. ICGC database was also used for external validation. ResultsA total of 39 lipid metabolism-related DEGs were analyzed that showed significant enrichment in the phospholipid metabolic process, glycerolipid metabolic process and glycerophospholipid pathways. Seven lipid metabolism genes (ELOVL3, LCLAT1, ME1, PPARGC1A, PTDSS2, SRD5A3, SLC2A1) closely related with prognosis were identified to construct the signature. Patients with low-risk scores showed better survival rates, which was also validated in the ICGC database. ConclusionWe established a signature composed of seven lipid metabolism-related genes to predict the prognosis of HCC patients, providing a new biomarker for the diagnosis and treatment of HCC.