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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Inflammation

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1526174

This article is part of the Research Topic Molecular Mechanisms and Therapeutic Strategies in Inflammation View all 13 articles

Comprehensive Integration of Diagnostic Biomarker Analysis and Immune Cell Infiltration Features in Sepsis via Machine Learning and Bioinformatics Techniques

Provisionally accepted
Liuqing Yang Liuqing Yang 1*Rui Xuan Rui Xuan 1*Dawei Xu Dawei Xu 1*Aming Sang Aming Sang 1*jing Zhang jing Zhang 1Xinyi Li Xinyi Li 1*Xujun Ye Xujun Ye 2*Yanfang Zhang Yanfang Zhang 2*
  • 1 Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
  • 2 Department of Geriatrics, Zhongnan Hospital, Wuhan University, Wuhan, Hebei Province, China

The final, formatted version of the article will be published soon.

    Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfunction. Despite medical advancements, the critical need for research into dependable diagnostic markers and precise therapeutic targets. We screened out five gene expression datasets (GSE69063, GSE236713, GSE28750, GSE65682 and GSE137340) from the Gene Expression Omnibus. First, we merged the first two datasets. We then identified differentially expressed genes (DEGs), which were subjected to KEGG and GO enrichment analyses. Following this, we integrated the DEGs with the genes from key modules as determined by Weighted Gene Co-expression Network Analysis (WGCNA), identifying 262 overlapping genes. 12 core genes were subsequently selected using three machine-learning algorithms: random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVW-RFE). The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. CIBERSORT was utilized to evaluate the inflammatory and immunological condition of sepsis. Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we identified the chemical constituents of these three herbs and their target genes. We found that CD40LG is not only one of the 12 core genes we identified, but also a common target of the active components quercetin, luteolin, and apigenin in these herbs. We extracted the common chemical structure of these active ingredients -flavonoids. Through docking analysis, we further validated the interaction between flavonoids and CD40LG. Lastly, blood samples were collected from healthy individuals and sepsis patients, with and without the administration of Xuebijing, for the extraction of peripheral blood mononuclear cells (PBMCs). By qPCR and WB analysis. We observed significant differences in the expression of CD40LG across the three groups. In this study, we pinpointed candidate hub genes for sepsis and constructed a nomogram for its diagnosis. This research not only provides potential diagnostic evidence for peripheral blood diagnosis of sepsis but also offers insights into the pathogenesis and disease progression of sepsis.

    Keywords: Sepsis, bioinformatics, machine learning, biomarkers, Immune Cell Infiltration

    Received: 11 Nov 2024; Accepted: 14 Feb 2025.

    Copyright: © 2025 Yang, Xuan, Xu, Sang, Zhang, Li, Ye and Zhang. 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:
    Liuqing Yang, Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
    Rui Xuan, Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
    Dawei Xu, Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
    Aming Sang, Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
    Xinyi Li, Department of Anesthesiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
    Xujun Ye, Department of Geriatrics, Zhongnan Hospital, Wuhan University, Wuhan, 430071, Hebei Province, China
    Yanfang Zhang, Department of Geriatrics, Zhongnan Hospital, Wuhan University, Wuhan, 430071, Hebei Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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