ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1684774
Integrated Bioinformatics and Molecular Docking Analysis Reveal Potential Hub Genes and Targeted Therapeutics in Sepsis-Associated Acute Lung Injury
Provisionally accepted
- 1Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, China
- 2Taizhou Municipal Hospital, Taizhou, China
- 3The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- 4Taizhou Hospital of Zhejiang Province, Linhai, China
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Background: Sepsis-associated acute lung injury (SA-ALI) is a severe complication of sepsis with high mortality. This study aimed to identify key diagnostic genes and potential therapeutic drugs for SA-ALI. Methods: Transcriptomic data from GSE10474 and GSE32707 were integrated for differential expression and WGCNA analysis. Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking. Results: Six hub genes (PGM3, GDF15, GART, GFOD2, E2F2, ATP1B2) were identified and validated with elevated expression in SA-ALI. These genes were enriched in inflammation, immune regulation, oxidative stress, and tissue remodeling pathways, and showed significant correlations with specific immune cell subsets. Five candidate small molecules were predicted; molecular docking revealed Celastrol had the strongest binding to all six proteins, particularly GDF15 (-9.988 kcal/mol), while Thiostrepton showed strong binding to PGM3, GFOD2, and GDF15. Conclusion: Six diagnostic hub genes and two priority candidate drugs, Celastrol and Thiostrepton, were identified for SA-ALI, providing potential biomarkers and therapeutic targets.
Keywords: sepsis-associated acute lung injury (SA-ALI), Hub genes, machine learning, molecular docking, small-molecule drugs
Received: 13 Aug 2025; Accepted: 24 Sep 2025.
Copyright: © 2025 Chen, Mao, Cai, zhang, Zeng, Chen and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cheng Zheng, dr.zhengcheng@foxmail.com
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