• In silico analyses for potential key genes associated Hepatocellular Carcinoma
  • Zahra Boostan,1,*
    1. Department of biology, Faculty of Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran


  • Introduction: Hepatocellular cancer (HCC) is one of the common type of liver cancer which is the third cause of death in all kind of cancers. The main reason of HCC pathogenesis is aggregation of gene mutations that caused cellular and molecular modification. It is necessary to find biomarkers with high specificity and sensitivity to screen HCC. Microarray technology is used for identify genome mechanism in liver tumorigenesis. Using data from microarray and combine them with bioinformatic approaches prepare outstanding strategy for studying comparison between gene expression in cancer and normal sample in patients. In this study, we identify key genes associated with hepatocellular carcinoma and investigate their underlying molecular mechanisms.
  • Methods: In the current study, two microarray dataset (GSE36376, GSE14520) were downloaded from the Gene Expression Omnibus database (GEO). The fold change (FC) values of individual gene levels were calculated; differentially expressed genes (DEGs) with |FC| > 1 and P-value < 0.05 were considered to be significant. The Venn diagram was carried out for the overlapped part via Funrich software.
  • Results: A total of 172 overlapped upregulated genes and 193 downregulated genes were identified. To identify the most influential genes in each group, we calculated the Matthews correlation coefficient (MCC) for all upregulated and downregulated genes and selected the top 20 genes with the highest MCC values. Analysis showed that up-regulated genes involve in the metabolism of lipids and lipoproteins, cholesterol biosynthesis I, mesenchymal-to-epithelial transition. Down-regulated genes mainly associate with alpha4 beta1 integrin signaling events, Integrin family cell surface interactions, beta1 integrin cell surface interactions and p53 pathway.
  • Conclusion: These in silico predictions will provide useful information in selecting the target genes that are likely to have functional impact on the HCC and may serve as potential diagnostic biomarkers in HCC patients.
  • Keywords: hepatocellular cancer; biomarker; microarray; MCC