Development of a prognostic model based on immunogenomic landscape analysis of hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and the systematic exploration of its prognostic indicators is urgently needed. In this study, we obtained 12 IRGs for the construction of a risk score prediction model in HCC by bioinformatics analysis. Methods Differentially expressed genes were screened using the R software edgeR package. Functional enrichment analysis was performed through gene ontology analyses as well as the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Single factor and multi-factor Cox analysis were employed for survival analysis. We used the Timer software to examine the correlation between risk score and tumor-infiltrating immune cells. Results We identified 3,215 up-regulated and 1,044 down-regulated genes in HCC tissues based on a cohort from The Cancer Genome Atlas (TCGA). Differentially expressed immune-related genes (IRGs) and survival-associated IRGs were further identified. We also integrated multivariate Cox regression analyses to obtain 12 IRGs for the construction of a risk score prediction model, whose performance was verified using the Kaplan-Meier survival and receiver operating characteristic curve analyses. Our findings suggest that the risk score was associated with clinical characteristics and the infiltration of immune cells in HCC patients. Conclusions We obtained a risk score prediction model of 12 IRGs in HCC by bioinformatics analysis and confirmed its performance.