Genetic-driven biomarkers for liver fibrosis through bioinformatic approach

Abstract

Abstract Background Liver fibrosis is a widespread chronic liver ailment linked to substantial mortality and limited therapeutic options. An in-depth comprehension of the genetic underpinnings of liver fibrogenesis is crucial for the development of effective management and treatment approaches. Results Using bioinformatics tools and the DisGeNET database, we pinpointed 105 genes significantly linked to liver fibrosis. Subsequently, we conducted functional assessments, incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the STRING database, to construct protein–protein interaction networks (PPI) for these 105 liver fibrosis-associated genes. These analyses were executed via the WebGestalt 2019 online platform. We employed Cytoscape plugins, MCODE, and CytoHubba, to identify potential biomarker genes from these functional networks. Noteworthy hub genes encompassed TGF-β1, MMP2, CTNNB1, FGF2, IL6, LOX, CTGF, SMAD3, ALB, and VEGFA. TGF-β1 and MMP-2 exhibited substantial promise as liver fibrosis biomarkers, as denoted by their high systemic scores determined through the MCC algorithm in the CytoHubba methodology. Conclusions In summary, this study presents a robust genetic biomarker strategy that may prove invaluable in the identification of potential liver fibrosis biomarkers.

Keywords

• Bioinformatics
• Biomarkers
• Liver fibrosis
• TGF-β1
• MMP-2