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Updated: May 12, 2026
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Screening of Sepsis Diagnostic Biomarkers Based on Fumarate Metabolism-Related Genes with Analysis of Immune
Ming Li1, Tingting Zhao2, Jing Sun1
1Department of Emergency Medicine, Deqing People's Hospital, Huzhou, Zhejiang, China.
Background:
Sepsis is a major global health challenge characterized by a complex pathogenesis involving an early hyperinflammatory phase followed by a subsequent immunosuppressive state. Recent studies have revealed that dysregulation of fumarate metabolism plays a central role in immune dysregulation during sepsis, making related genes promising candidates as novel diagnostic biomarkers and therapeutic targets.
Methods:
Three sepsis datasets (GSE65682, GSE95233, GSE131761) were analyzed to identify differentially expressed fumarate metabolism-related genes. Key genes were selected via machine learning (Least Absolute Shrinkage and Selection Operator, Support Vector Machine, Boruta) to construct a diagnostic model, validated by Receiver Operating Characteristic, nomogram, and Decision Curve Analysis. Immune infiltration, functional enrichment, subtype analysis, and a ceRNA network were further explored.
Results:
This study identified four core diagnostic genes for sepsis related to fumarate metabolism (EPHX2, S100A8, TXN, ANXA3). Immune analysis revealed increased infiltration of neutrophils and M1 macrophages in sepsis patients, alongside a reduction in adaptive immune cells such as CD8+ T cells. Molecular subtyping based on these genes identified two sepsis subtypes with distinct immune characteristics, and a relevant ceRNA regulatory network was constructed.
Conclusion:
This study constructs a diagnostic model for sepsis based on fumarate metabolism-related genes, linking metabolic reprogramming to immune dysregulation and offering biomarkers and theoretical support for personalized treatment.
