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Peer-reviewedCC BY 4.0Frontiers in Bioinformatics

Identification and validation of biomarkers related to oxidative stress in pediatric sepsis utilizing WGCNA and multiple machine learning algorithms

  • Yumeng Liu1
  • Gang Luo1
  1. 1Department of Pediatrics, Liaoning Maternal and Child Health Hospital, Shenyang, Liaoning, China
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Abstract

Background: This research aimed to identify novel oxidative stress (OS)–related biomarkers and develop predictive models for the early detection of pediatric sepsis.

Methods: Five open-access datasets from the gene expression omnibus database were analyzed, which included peripheral whole blood samples from children with sepsis and healthy controls. Using bioinformatics approaches, we identified differentially expressed genes (DEGs) associated with OS. We then combined weighted gene co-expression network analysis (WGCNA) and multiple machine learning algorithms (ML) to identify seven candidate genes, constructed a protein–protein interaction (PPI) network to screen six hub genes, and evaluated their correlations with the immune microenvironment. We subsequently evaluated the diagnostic performance of the hub genes using training datasets (GSE26440 and GSE26378) and validation datasets (GSE13904, GSE8121, and GSE9692). Moreover, molecular docking analysis revealed interactions between hub gene–encoded proteins and small-molecule compounds, which helps predict drug sensitivity.

Results: The study identified 36 differentially expressed oxidative stress-related genes (OSRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted these genes’ roles in regulating inflammatory responses and reactive oxygen species metabolism. Seven candidate genes were identified using machine learning algorithms. Subsequently, through protein-protein interaction (PPI) network analysis, six hub genes, namely, GADD45A, HP, MAPK14, PGD, RETN, and S100A9, were identified as having significant diagnostic potential. Immune cell deconvolution analysis of peripheral blood transcriptomes suggested that hub genes related to oxidative stress may be linked to alterations in the composition of peripheral blood immune cells. The diagnostic performance of the six hub genes was validated with area under the curve (AUC) values between 0.908 and 0.982, showing their promising diagnostic performance. Additionally, molecular docking analysis suggested that small molecules Doxycycline, Ralimetinib mesylate, Tasquinimod might interact with the identified hub genes, which opens up new treatment options.

Conclusion: Our research not only reveals potential diagnostic biomarkers of pediatric sepsis but also sets the stage for future studies. These studies aim to translate our findings into clinical practice, enabling earlier and more accurate diagnoses as well as targeted therapies to improve outcomes for children.

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Cite

BibTeXRIS
BibTeX
@article{liu2026identification,
  title = {{Identification and validation of biomarkers related to oxidative stress in pediatric sepsis utilizing WGCNA and multiple machine learning algorithms}},
  author = {Yumeng Liu and Gang Luo},
  journal = {Frontiers in Bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.3389/fbinf.2026.1745381},
  url = {https://doi.org/10.3389/fbinf.2026.1745381},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Identification and validation of biomarkers related to oxidative stress in pediatric sepsis utilizing WGCNA and multiple machine learning algorithms
AU  - Yumeng Liu
AU  - Gang Luo
PY  - 2026
DA  - 2026/10/12
JO  - Frontiers in Bioinformatics
DO  - 10.3389/fbinf.2026.1745381
UR  - https://doi.org/10.3389/fbinf.2026.1745381
AB  - Background: This research aimed to identify novel oxidative stress (OS)–related biomarkers and develop predictive models for the early detection of pediatric sepsis.  Methods: Five open-access datasets from the gene expression omnibus database were analyzed, which included peripheral whole blood samples from children with sepsis and healthy controls. Using bioinformatics approaches, we identified differentially expressed genes (DEGs) associated with OS. We then combined weighted gene co-expression network analysis (WGCNA) and multiple machine learning algorithms (ML) to identify seven candidate genes, constructed a protein–protein interaction (PPI) network to screen six hub genes, and evaluated their correlations with the immune microenvironment. We subsequently evaluated the diagnostic performance of the hub genes using training datasets (GSE26440 and GSE26378) and validation datasets (GSE13904, GSE8121, and GSE9692). Moreover, molecular docking analysis revealed interactions between hub gene–encoded proteins and small-molecule compounds, which helps predict drug sensitivity.  Results: The study identified 36 differentially expressed oxidative stress-related genes (OSRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted these genes’ roles in regulating inflammatory responses and reactive oxygen species metabolism. Seven candidate genes were identified using machine learning algorithms. Subsequently, through protein-protein interaction (PPI) network analysis, six hub genes, namely, GADD45A, HP, MAPK14, PGD, RETN, and S100A9, were identified as having significant diagnostic potential. Immune cell deconvolution analysis of peripheral blood transcriptomes suggested that hub genes related to oxidative stress may be linked to alterations in the composition of peripheral blood immune cells. The diagnostic performance of the six hub genes was validated with area under the curve (AUC) values between 0.908 and 0.982, showing their promising diagnostic performance. Additionally, molecular docking analysis suggested that small molecules Doxycycline, Ralimetinib mesylate, Tasquinimod might interact with the identified hub genes, which opens up new treatment options.  Conclusion: Our research not only reveals potential diagnostic biomarkers of pediatric sepsis but also sets the stage for future studies. These studies aim to translate our findings into clinical practice, enabling earlier and more accurate diagnoses as well as targeted therapies to improve outcomes for children.
SN  - 2673-7647
ER  -