Computational identification of antifibrotic microRNAs targeting hepatic stellate cell activation

The complexity of liver fibrosis highlights the need for therapeutic strategies capable of modulating multiple pathways simultaneously. MicroRNAs (miRNAs), as multitarget regulators of gene expression, represent promising antifibrotic candidates. Here, we developed an integrative workflow combining cellular models, quantitative proteomics, and bioinformatic analyses to prioritize antifibrotic miRNAs. Activation of LX-2 hepatic stellate cells with TGF-β1 induced a profibrotic phenotype and enabled proteomic identification of upregulated proteins. These proteins were integrated with two miRNA-target prediction approaches, including experimentally validated interactions from miRTarBase and computational predictions generated using the miRNA binding sites (MBS) tool. The collagen-maturation enzyme prolyl 4-hydroxylase subunit α2 (P4HA2) was selected as a proof-of-concept target. Screening of five miRNAs predicted to target P4HA2 identified hsa-miR-9-5p as the most promising candidate, significantly reducing P4HA2 protein levels and suppressing profibrotic marker expression during LX-2 cell activation. Direct interaction with the P4HA2 3′UTR was confirmed by a luciferase reporter gene assay. These effects were further confirmed in HepG2/LX-2 co-culture liver spheroids, where collagen modulation was observed in a subset of cells, likely corresponding to the stellate cell compartment. Collectively, these findings establish our computationally guided prioritization strategy as a platform for identifying therapeutic miRNAs with broad applicability across diverse disease contexts.