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DOI 10.3389/fbinf.2025.1613985

Медицинская библиотека · Frontiers in bioinformatics · 2025

Identifying novel therapeutic targets for non-alcoholic fatty liver disease using bioinformatics approaches: from drug repositioning to traditional Chinese medicine.

Zhang Jingmin, Meng Tianwei, Gao Weiqi, Li Xinghua, Xu Juan

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Аннотация

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition with limited effective treatments, necessitating novel therapeutic strategies. Bioinformatics offers a promising approach to identify new targets by analyzing gene expression and drug interactions.

OBJECTIVE

This study aims to identify novel therapeutic targets for NAFLD through bioinformatics, focusing on drug repositioning and traditional Chinese medicine (TCM) components.

METHODS

Three NAFLD-related gene expression datasets (GSE260666, GSE126848, GSE135251) were analyzed to identify differentially expressed genes. Protein-protein interaction networks were constructed using STRING and visualized with Cytoscape. Pathway enrichment analysis was performed, and drug-gene interactions were explored using the DGIdb database. TCM components were screened via the HERB database, with molecular docking conducted to assess binding affinities.

RESULTS

Key hub genes (CXCL2, CDKN1A, TNFRSF12A, HGFAC) were identified, with significant enrichment in cell proliferation and PI3K-Akt signaling pathways. Cyclosporine emerged as a potential repurposed drug, while TCM components (curcumin, resveratrol, berberine) showed strong binding affinities to NAFLD targets.

CONCLUSION

Cyclosporine and TCM compounds are promising candidates for NAFLD treatment, warranting further experimental validation to confirm their therapeutic potential.

Для цитирования: Zhang Jingmin, Meng Tianwei, Gao Weiqi, Li Xinghua, Xu Juan. Identifying novel therapeutic targets for non-alcoholic fatty liver disease using bioinformatics approaches: from drug repositioning to traditional Chinese medicine.. Frontiers in bioinformatics. 2025. DOI: 10.3389/fbinf.2025.1613985.
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