Identifying Potential Three Key Targets Gene for Septic Shock in Children Using Bioinformatics and Machine Learning Methods

Aug 5, 2026

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Septic shock in children is an infection-driven, low-immunity condition with very high mortality, and its lethal death mechanisms remain insufficiently defined. This study applies bioinformatics and machine learning to discover key genes and biological pathways linked to fatal sepsis, aiming to support early risk assessment and a theoretical basis for rational follow-up TCM intervention. Differential expression analysis, GO/KEGG enrichment, and protein-protein interaction network screening are integrated with three machine learning models, followed by ROC validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Introduction

Definition and clinical significance

Epidemiology and mortality in children

Need for early intervention and biomarker discovery

TYPE Original Research PUBLISHED 17 June 2025 DOI 10.3389/fimmu.2025.1586584 OPEN ACCESS EDITED BY Swapan K. Nath, Oklahoma Medical Research Foundation, United States REVIEWED BY Ivana Kawikova, National Institute of Mental Health, Czechia Guojun Qian, Guangzhou Medical University, China *CORRESPONDENCE Wei Guo [19904509966@163.com](19904509966@163.com)[ ](19904509966@163.com)Hong Chen [chenrongsheng1977@163.com](chenrongsheng1977@163.com) RECEIVED 03 March 2025 ACCEPTED 30 May 2025 PUBLISHED 17 June 2025 CITATION Guo W, Chen H, Wang F, Chi Y, Zhang W, Wang S, Chen K and Chen H (2025) Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods. Front. Immunol. 16:1586584 . doi: 10.3389/fimmu.2025.1586584 COPYRIGHT © 2025 Guo, Chen, Wang, Chi, Zhang, Wang, Chen and Chen. 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) and the copyright owner(s) 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. Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods Wei Guo 1*, Hao Chen 2, Feng Wang 2, Yingjiao Chi 3, Wei Zhang 1, Shan Wang 4, Kezhu Chen 5 and Hong Chen 1* 1 Department of Pediatrics, First Afﬁliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China, 2 Department of Surgery, Heilongjiang Academy of Traditional Chinese Medicine, Harbin, China, 3 Department of Pediatrics, Harbin First Hospital, Harbin, China, 4 Ning ‘an Hospital of Traditional Chinese Medicine Pediatrics, Ning ‘an, China, 5Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China Background: Septic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. However, the mechanism of death from sepsis in children remains unclear. This study aims to use bioinformatics and machine learning algorithms to identify key genes and pathways associated with fatal sepsis in children, and provide theoretical basis for rational drug use in follow-up TCM treatment. Methods: Gene expression proﬁles were obtained from the GEO database (GSE4607) for 15 blank patients and 14 children with sepsis death. Differentially expressed genes (DEGs) were enriched by GO and KEGG pathways. Construct and visualize protein-protein interaction (PPI) networks to identify candidate genes responsible for fatal sepsis in children. Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. ROC curve was drawn for core genes to clarify the diagnostic value of genetic markers. Results: Analysis of differences in the preprocessed dataset identiﬁed 83 genes, including 78 up-regulated genes and 5 down-regulated genes. 17 candidate genes were screened by protein interaction network analysis. Three machine learning algorithms LASSO, random forest(RF), and support vector machine recursive feature elimination (SVM-RFE) were used to ﬁnally screen out three core genes: CD163, MCEMP1 and RETN. CD163, MCEMP1 and RETN may jointly regulate complement and coagulation cascades, toll like receptor signaling pathway, graft versus host disease, type I diabetes mellitus. Conclusion: In this study, three core genes(CD163, MCEMP1andRETN)that lead to sepsis death in children were screened out, providing a new understanding of the lethal mec

FAQs

What problem does the study address in pediatric septic shock?

It targets the high mortality of septic shock in children and the incomplete understanding of mechanisms leading to death, emphasizing the need for early prediction and biomarkers.

How were candidate genes identified in the workflow?

Gene expression profiles from GEO were analyzed for differentially expressed genes, enriched by GO/KEGG, mapped onto PPI networks, and then screened using intersection across three machine learning models.

Which three core genes were finally selected and what do the results suggest?

The study screened CD163, MCEMP1, and RETN as core genes, suggesting they may jointly regulate pathways related to complement/coagulation cascades and inflammatory signaling relevant to fatal outcomes.

Aug 5, 2026

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