Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (-6.32 kcal/mol) and diosmin (-6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV.
Keywords: HCV diagnosis biomarker; machine learning algorithms; molecular docking; therapeutic target.