Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning

Qinghui Sun, Hai-Li Zhang, Yichao Wang, Hao Xiu, Yufei Lu, Na He, Li Yin

Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection approaches. Differential expression analysis, pathway enrichment, and network analyses were employed to uncover potential biomarkers and molecular mechanisms. WGCNA identified modules such as MEbrown4 and MEblack, which strongly correlated with sepsis progression (r > 0.7, p < 0.01). Differential expression analysis highlighted up-regulated genes like CD177 and down-regulated genes like LOC440311. KEGG analysis revealed significant pathways including neuroactive ligand–receptor interaction, PI3K-Akt signaling, and MAPK signaling. Gene ontology analysis showed involvement in immune-related processes such as complement activation and antigen binding. Protein–protein interaction network analysis identified hub genes such as TNFSF10, IGLL5, BCL2L1, and SNCA. Feature selection methods (random forest, LASSO regression, SVM-RFE) consistently identified top predictors like TMCC2, TNFSF10, and PLVAP. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for sepsis progression, with AUC values of 0.973 (TMCC2), 0.969 (TNFSF10), and 0.897 (PLVAP). Correlation analysis linked key genes such as TNFSF10, GUCD1, and PLVAP to pathways involved in immune response, cell death, and inflammation. This integrative transcriptomic analysis identifies critical gene modules, pathways, and biomarkers associated with sepsis progression. Key genes like TNFSF10, TMCC2, and PLVAP genes show strong diagnostic potential, providing novel insights into sepsis pathogenesis and offering promising targets for future therapeutic interventions. Among these, TNFSF10 and PLVAP are known to encode secreted proteins, suggesting their potential as circulating biomarkers. This enhances their translational relevance in clinical diagnostics.