Abstract
Hepatocellular carcinoma (HCC) is a carcinoma of epithelial origin. While there are several factors, specific genetic and epigenetic landscapes define the initiation and progression of HCC. Genetic mutations, particularly missense mutations, often act as predictors of the onset of cancers, including HCC. Specifically, mutations associated with telomerase, TP53, and beta-catenin (CTNNB1) are among the three most commonly mutated genes in HCC. These genetic mutations define specific subtypes of HCC, exhibiting specific epigenetic expression patterns in terms of miRNA expression and the interactome. In our current study, we performed a differential expression analysis of multiple miRNAs among three different cell lines, HepG2, Huh7, and QGY7703, which exhibit different mutational patterns. This is the first study to characterize HCC cell lines based on miRNA expressions. We also identified the enriched pathways associated with the significantly differentially expressed miRNAs, bioinformatically predicted their targets, and characterized the interactomes. Additionally, we classified the small RNA sequencing data available from the publicly available dataset based on the mutational status of cancer samples and computed the overlaps of miRNAs exhibiting similar expression patterns consistent with the in vitro data, predicted the top hub genes and their associated pathways, and predicted their drug targets using an integrated bioinformatic approach.