Screening of Sepsis Diagnostic Biomarkers Based on Fumarate Metabolism-Related Genes with Analysis of Immune Infiltration and Subtype Identification.

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.