Abstract

BACKGROUND: Worldwide, hepatocellular carcinoma (HCC) accounts for 80–90% of all cases of primary liver cancer, and is one of the ten most common malignancies. This study used bioinformatics analysis to identify genes associated with patient outcome in stages I–IV HCC and the gene pathways that distinguished between normal liver and liver cells and HCC and human HCC cell lines.

MATERIAL AND METHODS: Target genes were defined as those that had marketed drugs or drugs under development targeting a specific gene and acquired from the Clarivate Analytics Integrity Database. Differential expression gene analysis, co-expression network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, survival analysis and receiver operating characteristic (ROC) curve analysis were used to explore the similarities and differences in gene expression profiles, functional associations, and survival in stage I–IV HCC. Normal liver cells (HL-7702) and HCC cell lines (HepaRG, HepG2, SK-Hep1, and Huh7) were studied using Western blot and quantitative reverse transcription PCR (RT-qPCR).

RESULTS: Hierarchical gene clustering identified target genes that distinguished between HCC and normal liver tissue. For stages I–IV HCC, there were seven commonly upregulated target genes EPHB1, LTK, NTRK2, PTK7, TBK1, TIE1, and TLR3, which were mainly involved in immune and signaling transduction pathways. PTK7 was highly expressed in stage I–IV HCC and was an independent prognostic marker for reduced overall survival (OS).

CONCLUSIONS: Bioinformatics analysis, combined with patient survival analysis, identified PTK7 gene expression as a potential therapeutic target and prognostic biomarker for all stages of HCC.

Keywords: hepatocellular carcinoma, Gene Expression, Gene Targeting, Biomarkers, Tumor, Carcinoma, Hepatocellular, Cell Adhesion Molecules, Cluster Analysis, Computational Biology, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Liver Neoplasms, Neoplasm Staging, Prognosis, Protein Interaction Maps, ROC Curve, Receptor Protein-Tyrosine Kinases, transcriptome