Integrated bioinformatics analysis reveals cytokine dysregulation, pathway alterations, and machine learning identification of diagnostic biomarkers in sepsis

Sepsis, a life-threatening dysregulated host response to infection, involves complex cytokine signaling. Comprehensive bioinformatics analysis of cytokine activity, associated pathways, and immune alterations in sepsis is warranted. Using the sepsis dataset GSE26378 from GEO, we analyzed differential cytokine pathway activity with ssGSEA and identified differentially expressed genes (DEGs). Cytokine-related genes (CRGs) were extracted and overlapped with DEGs. Protein–Protein Interaction (PPI) network analysis and functional enrichment were performed on differentially expressed CRGs. Cytokine activity scores and pathway activities were quantified using Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed with MCP-counter. Machine learning algorithms (Random Forest, LASSO, SVM) identified diagnostic biomarkers, validated using an independent dataset (GSE26440) and ROC analysis. Cytokine/cytokine receptor pathways were significantly upregulated in sepsis. We identified 617 DEGs and 46 differentially expressed CRGs. Cytokine activity scores were significantly elevated in sepsis and strongly correlated with heightened activity in inflammatory pathways (e.g. TLR, IL-1R, NF-κB, JAK/STAT, hypoxia) and metabolic pathways (e.g. glycolysis, PI3K/AKT/mTOR). Immune analysis showed decreased T cells, NK cells, B cells, and cytotoxic lymphocytes, alongside increased neutrophils and endothelial cells; neutrophil infiltration positively correlated with cytokine scores. Machine learning identified four core genes (C3AR1, XCL1, CSF2RA, IL2RB), consistently dysregulated in sepsis across datasets and demonstrating robust diagnostic accuracy. This integrated bioinformatics study indicates heightened cytokine activity, profound alterations in inflammatory and metabolic pathways, and a dysregulated immune cell landscape in sepsis. The identified hub genes and the four-gene biomarker panel show potential as diagnostic tools, offering insights into sepsis pathophysiology. Insight box This study integrates multi-omics bioinformatics (ssGSEA, GSVA, PPI, immune deconvolution) and machine learning (RF, LASSO, SVM) to dissect sepsis pathophysiology. Innovatively, we quantify cytokine pathway hyperactivity, linking it to inflammatory/metabolic dysregulation (TLR, NF-κB, glycolysis) and immune imbalance. A novel four-gene panel (C3AR1, XCL1, CSF2RA, IL2RB) was identified and validated as a robust diagnostic biomarker, bridging cytokine signaling with clinical utility. The findings provide mechanistic insights into sepsis-driven immune-metabolic crosstalk and offer translational potential for early diagnosis and targeted therapy.