Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis

Abstract

Background:: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it. Methods:: GSE57065, GSE65682, GSE145227, and GSE54514 from Gene Expression Omnibus (GEO) were derived for this study. Secretory pathways single sample gene set enrichment analysis (ssGSEA) scores in sepsis and normal samples were exposed. Gene modules associated with secretory pathways were selected by weighted gene coexpression network analysis (WGCNA) for Protein-Protein Interaction Networks (PPI) assessment, and crossover genes in both were evaluated by eXtreme Gradient Boosting (XGBoost) model in feature selection to identify hub genes in sepsis. In addition, we explored the immune cells and signaling pathways regulated by hub genes. objective: we aim to build a diagnosis system for sepsis Results:: Remarkable dysregulation of secretory pathways was demonstrated in sepsis. The secretory pathways-associated gene modules were intimately involved in cytokine and immune responses in infection. Four crossover genes (CD163, FCER1G, C3AR1, ARG1) were present in WGCNA and PPI, and training in the XGBoost model revealed the best diagnostic performance of these 4 genes, meaning that these genes were the hub genes for sepsis. The 4-hub genes showed a significant negative correlation with T cell activity and a significant positive correlation with inflammatory immune cells. In addition, we found that the 4-hub genes markedly positively regulated INFLAMMATORY RESPONSE, IL6 JAK STAT3 SIGNALING. Conclusion:: Based on WGCNA, PPI, and XGBoost models, we identified hub genes that play an important regulatory role in sepsis. We also developed novel molecular models for the diagnosis of sepsis.