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Volume 32, Issue 7
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

The hallmark of non-alcoholic fatty liver disease (NAFLD) is aberrant buildup of triglycerides (TGs) in hepatocytes. Many genes promote NAFLD development. Using bioinformatics tools, we investigated the possible effect of statins on genes involved in NAFLD progression.

Methods

Protein interactions of statins and NAFLD were searched in gene-drug and gene-disease databases. A Protein-Protein interaction (PPI) network was constructed to find hub genes and Molecular Complex Detection (MCODE) of NAFLD-related genes. Shared protein targets between protein targets of statins and NAFLD-associated genes were identified. Next, targets of each statin were assayed with all modular clusters in the MCODEs related to NAFLD. Biological process and pathway enrichment analysis for shared proteins was performed.

Results

Screening protein targets for conventional statins and curated NAFLD-related genes identified 343 protein targets and 70 genes, respectively. A Venn diagram of NAFLD-related genes and protein targets of statins showed 24 shared proteins. The biological pathways on KEGG enrichment associated with the 24 shared protein sets were evaluated and included cytokine-cytokine receptor interaction, adipocytokine, PPAR, TNF and AMPK signaling pathways. Gene Ontology analysis showed major involvement in lipid metabolic process regulation and inflammatory response. PPI network analysis of 70 protein targets indicated 13 hub genes (PPARA, IL4, CAT, LEP, SREBF1, PRKCA, CYP2E1, NFE2L2, PTEN, NR1H4, ADIPOQ, GSTP1 and TGFB1). Comparing all seven statins with the three MCODE clusterings and 13 hub genes revealed that simvastatin as the most associated statin with NAFLD.

Conclusion

Simvastatin has the most impact on NAFLD-related genes versus other statins.

© 2025 Bentham Science Publishers
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2023-10-12
2025-04-06

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Statins as a Potential Treatment for Non-alcoholic Fatty Liver Disease: Target Deconvolution using Protein-protein Interaction Network Analysis
Curr. Med. Chem. 32, 1355 (2025); https://doi.org/10.2174/0929867331666230829164832
/content/journals/cmc/10.2174/0929867331666230829164832
/content/journals/cmc/10.2174/0929867331666230829164832
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  • Article Type:     Research Article
Keyword(s): database; gene ontology; KEGG; NAFLD; Statins; STITCH

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