Analysis of epigenetic factors determining the hepatocellular carcinoma prognosis with advanced bioinformatics methods

Aim: The aim of the present study is to evaluate the molecular genetic mechanisms that play a role in the biologic behavior of HCC by analyzing of the transcriptomic and epigenetic signatures of the tumors. Material and Method: Transcriptomic data were downloaded from the NCBI GEO database. The expression differences between the GSE46444 and GSE63898 data sets were analyzed using the GEO2R. The genes that have shown and expression difference were further evaluated using GO and KEGG metabolic pathway analysis websites. WGBS and MeDIP-Seq data sets were downloaded from the NCBI SRA database. WGBS and MeDIP-Seq data were analyzed by using Bismark and QSEA, respectively. The methylation differences between the groups were evaluated by using the functional enrichment analysis. Results: In the GSE46444 data set, 80 genes were upregulated, and 315 genes were down-regulated in the HCC tissue when compared to the non-tumorous cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were down-regulated in the cirrhotic tissue when compared to the HCC tissues. WGBS showed that 20 protein coding loci were hypermethylated and majority of the hypomethylated regions were non-protein coding/protein coding. The methylated residues of the HCC, cirrhotic and healthy tissues were statistically comparable. The MeDIP-Seq was comparatively performed on the HCC ad non-HCC tissues and hypermethylated or hypomethylated areas were determined to be protein coding regions. The functional enrichment analysis showed that these genes were related with peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras and PI3K/AKT signal transduction pathways. Conclusions: The results of the present study using the transcriptomic and epigenetic methods identified various genes that were effective in the biologic behavior of HCC. These can be potential candidates for developing targeted therapy. Keywords: Bioinformatic analysis, Epigenetics, Genetics, Hepatocellular carcinoma, Transcriptomics,