Hepatitis C virus protein interaction network for HCV clearance and association of DAA to HCC occurrence via data mining approach: A systematic review and critical analysis.

HCV has been associated with a pro-inflammatory state, which predisposes to hepatocellular carcinoma (HCC). However, the different molecular mechanisms underlying the effect of HCV infection on HCC progression remain unclear. Although HCV infection illustrates the potential role of host genetics in the outcome of infectious diseases, there is no clear overview of some single nucleotide polymorphisms (SNPs) influencing spontaneous or treatment-induced HCV eradication. We studied the possible role of HCV infection in the processes of HCC initiation and performed a systematic analysis using data mining approaches to identify host polymorphisms associated with treatment response and HCC development using topological analysis of protein-proteins interactions (PPI) networks. On the basis of our analysis performed, we identified key hub proteins related to HCV-treatment response infection and to HCC development. Host genetic polymorphisms, such as inosine triphosphatase (ITPA), interferon, lambda 3 (IFNL3), Q5 interferon, lambda 4 (IFNL4), toll-like receptors (TLRs) and interferon-stimulated gene 15 (ISG-15), were identified as key genes for treatment prediction and HCC evolution. By comparing unique genes for HCV-treatment response and genes particular to HCV-HCC development, we found a common PPI network that may participate in more extensive signalling processes during anti-HCV treatment, which can play important roles in modulating the immune response to the occurrence of HCC. Data mining is an effective tool for identifying potential regulatory pathways involved in treatment response and HCC development. Our study may contribute to a better understanding of HCV immunopathogenesis and highlights the complex role of host genetics in HCV clearance.