Objectives: The current study aimed to identify genes that show differential expression in tumor tissue by performing bioinformatic analysis from matched tumor and non-tumoral liver tissue samples obtained from HBV- HCC patients.
Methods: mRNA data from 21 HBV-HCC patients were used in this open-access database-based study. The mRNA sequence data were obtained from 21 pairs of tumors and non-tumoral liver tissue samples. Gene expression analysis was used in bioinformatics analyses and log2FC value was used to identify genes showing up- and down-regulation. To illustrate differentially expressed genes, the volcano plot was utilized.
Results: Our analysis showed that many genes showed quite different expression levels in tumor tissues. Among these genes, the genes that showed very high fold upregulation were GNG4, IGF2BP1, GPC3, PEG10, AFP, SPINK1, EPS8L3, MYCN, DUSP9, and DKK1 genes, respectively. The down-regulated genes were CNDP1, WAKMAR1, LINC01818, TH, LINC01093, MARCO, LOC101927078, LOC105372263, FCN2, and CLEC4M.
Conclusion: Our study defined various genes that might be utilized as potential biomarkers for HBV-related HCC. Targeted treatment for these genes can be developed and verified for efficacy in treatment.