An integrative transcriptomic analysis for prognosis and tumor microenvironment in HBV/HCV-associated hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide; molecular biomarkers that reflect hepatitis B virus (HBV)/hepatitis C virus (HCV)-associated tumor biology and predict prognosis or therapeutic vulnerabilities are needed. The research is a secondary analysis of public data (secondary data analysis) based on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data to identify differentially expressed genes (DEGs) common to HBV-HCC, HCV-HCC, and non-viral HCC datasets. Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, protein-protein interaction (PPI) network analysis, and hub-gene selection were then performed. Differential expression, prognostic association, immune-infiltration, and drug-sensitivity correlations were also analyzed. Eighty common DEGs (73 upregulated, 7 downregulated) were identified. PPI topological analysis yielded 53 hub genes; five genes TOP2A, RACGAP1, ASPM, CENPF, and GPC3, were prioritized and validated as significantly upregulated in liver HCC and associated with poorer overall survival. Immune deconvolution showed consistent positive correlations between the hub genes and B cells and regulatory T cell subsets, and negative correlations with mucosal-associated invariant T (MAIT) cells, macrophages, and natural killer (NK)/monocyte signatures. Drug-gene correlation analysis revealed positive associations of TOP2A and RACGAP1 with sensitivity to mitogen-activated protein kinase inhibitors and negative correlations with several targeted inhibitors. The identified prognostically unfavorable hub genes in HBV/HCV-associated HCC are associated with an immunosuppressive microenvironment and with patterns of drug sensitivity.