ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms
Provisionally accepted
- The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Background: Identifying novel biomarkers for sepsis is essential for improving patient outcomes. Cuproptosis, a recently discovered form of cell death associated with various diseases, has an unclear relationship with sepsis.This study aimed to elucidate the expression patterns of cuproptosis-related genes(CRGs) in sepsis, identifying potential biomarkers and therapeutic targets. Methods: We investigated the expression patterns of cuproptosis-related genes in sepsis and performed consensus clustering. A diagnostic model for sepsis was constructed using weighted gene co-expression network analysis (WGCNA) combined with four machine learning algorithms. Prognosis-related genes were identified via Kaplan-Meier survival analysis and validated in septic mice. Results: We identified 28 differentially expressed CRGs and characterized a specific immune landscape. Our findings showed that sepsis samples could be divided into two clusters based on CRGs expression. We established a diagnostic model based on five key genes(SHKBP1,ICAM2,CTSD,SNX3, and SLC22A4), and Kaplan-Meier survival analysis revealed that SHKBP1 was significantly correlated with both the diagnosis and prognosis of sepsis. Conclusion: Our study provides a comprehensive analysis of CRGs expression in sepsis, establishes a diagnostic model, and identifies SHKBP1 as a biomarker for both diagnosis and prognosis prediction, offering new insights for sepsis management.
Keywords: Sepsis, SHKBP1, cuproptosis-related genes(CRGs), machine learning, weighted gene co-expression network analysis(WGCNA)
Received: 19 Sep 2025; Accepted: 18 Nov 2025.
Copyright: © 2025 Wu, Lu, Liu, Zhou, Li, Wang, Shan, Sheng 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:
Fan Wu, wufan_doctor@163.com
Dan Zhang, doctor_zhangdan@126.com
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.