BACKGROUND: Growing evidence shows that the tumor microenvironment plays a crucial role in the pathogenesis of hepatocellular carcinoma (HCC). The present work aimed to screen tumor microenvironment-related genes strongly related to prognosis and to construct a prognostic gene expression model for HCC.

MATERIAL AND METHODS: We downloaded gene expression data of 371 HCC patients in The Cancer Genome Atlas (TCGA). A novel ESTIMATE algorithm was applied to calculate immune scores and stromal scores for each patient. Then, the differentially-expressed genes (DEGs) were detected according to the immune and stromal scores, and tumor microenvironment-related genes were further explored. Univariate, Lasso, and multivariate Cox analyses were performed to build the tumor microenvironment-related prediction model.

RESULTS: Stromal and immune scores were calculated and were found to be correlated with the 3-year prognosis of HCC patients. DEGs were detected according to the stromal and immune scores. There were 49 genes with prognostic value in both TCGA and ICGC (International Cancer Genome Consortium) considered as prognostic tumor microenvironment-related genes. Univariate, Lasso, and multivariate Cox analyses were conducted. A novel 2-gene signature (IL18RAP and GPR182) was built for HCC 3-year prognosis prediction. The 2-gene signature was regarded as an independent prognostic predictor that was correlated with 3-year survival rate, as shown by Cox regression analysis.

CONCLUSIONS: This study offers a novel 2-gene signature to predict overall survival of patients with HCC, which has the potential to be used as an independent prognostic predictor. Overall, this study reveals more details about the tumor microenvironment in HCC and offers novel candidate biomarkers.

Keywords: Carcinoma, Hepatocellular, tumor microenvironment, Algorithms, Biomarkers, Tumor, Computational Biology, Datasets as Topic, Interleukin-18 Receptor beta Subunit, Liver Neoplasms, Receptors, G-Protein-Coupled, Survival Rate