Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression...

Area of Science:

• Genomics
• Immunology
• Bioinformatics

Background:

• Sepsis is a life-threatening condition with high mortality, driven by immune dysregulation.
• Identifying key genes and pathways is vital for improving sepsis diagnosis and treatment.

Purpose of the Study:

• To analyze transcriptomic data to identify critical genes, pathways, and biomarkers for sepsis progression.
• To uncover molecular mechanisms underlying sepsis pathogenesis.

Main Methods:

• Weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection on transcriptomic data from septic patients and controls.
• Differential expression analysis, pathway enrichment (KEGG, Gene Ontology), and protein-protein interaction network analysis.
• Receiver operating characteristic (ROC) analysis to assess predictive accuracy of identified biomarkers.

Main Results:

• WGCNA identified modules (MEbrown4, MEblack) correlating with sepsis progression.
• Key genes TNFSF10, TMCC2, and PLVAP were consistently identified as top predictors with high diagnostic accuracy (AUC > 0.89).
• Significant pathways included neuroactive ligand-receptor interaction, PI3K-Akt, and MAPK signaling; immune-related processes were also highlighted.

Conclusions:

• Integrative transcriptomic analysis reveals critical gene modules and pathways in sepsis progression.
• TNFSF10, TMCC2, and PLVAP show strong potential as diagnostic biomarkers for sepsis.
• TNFSF10 and PLVAP, as secreted proteins, are promising candidates for circulating biomarkers, enhancing clinical relevance.