Evidence map›Paper›PMID 40932836›Full record

ArticleFrontiers in bioinformatics2025

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

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

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jingmin ZhangShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Tianwei MengHeilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Weiqi GaoShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.
Xinghua LiDepartment of Pharmacy, Changzhi People's Hospital, Changzhi, China.
Juan XuShanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

bioinformaticscyclosporinedrug repositioningnon-alcoholic fatty liver diseasetraditional Chinese medicine

Identifiers

PMID40932836
PMCPMC12417881

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.