Screening of kinase-related genes as diagnostic biomarkers and immune infiltration analysis in sepsis

Sepsis is a systemic inflammatory syndrome that can lead to loss of organ function. Kinase-related genes (KRGs) modulate immune diseases by regulating inflammation, immune metabolism, and apoptosis. However, their specific role in Sepsis remains unexplored.

mRNA and single-cell sequencing data in sepsis were obtained from the Gene Expression Omnibus. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with sepsis, which were intersected with KRGs to determine candidate genes. Functional enrichment analyses (GO, KEGG, GSVA) were conducted, followed by biomarker selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression and the Boruta algorithm. Model performance was assessed via receiver operating characteristic (ROC) curves, and immune infiltration was assessed using CIBERSORT and ssGSEA. Consensus clustering defined kinase-associated molecular subtypes of sepsis, while CellChat analysis of single-cell data characterized intercellular communication patterns.

This study employed WGCNA and machine learning analyses to identify four key diagnostic genes for sepsis-ZAP70, TXK, TRRAP, and TRIM28-and constructed a highly accurate diagnostic model (AUC = 0.986). Immune infiltration analysis revealed significant associations with T cells, NK cells, and other cell types. Single-cell data analysis demonstrated increased proportions of platelets and neutrophils in sepsis, highlighted the prominent role of monocytes in intercellular communication, and showed widespread expression of TRIM28 in monocyte subsets. Additionally, two distinct molecular subtypes with significant immune differences were identified.

This study systematically analyzed KRGs in sepsis and identified four clinically valuable biomarkers. By integrating single-cell transcriptomic data, it offers novel insights into the pathogenesis of sepsis and proposes potential biomarkers and therapeutic targets.