Background: Sepsis is a life-threatening disease caused by the dysregulated host response to the infection, and
being the major cause of death to patients in intensive care unit (ICU).
Objective: Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the
development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other
factors. Hence, the analysis of mass data by bioinformatic tools and machine learning is a promising method for exploring
early diagnosis manners.
Methods: We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO)
and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets
on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment
based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI)
network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type.
Results: Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis
identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub
genes was used to assess GSE95233 and GSE95233 datasets, and the area under curve (AUC) of ROC are 0.900 and 0.7988,
respectively, which confirmed the efficacy of this model.
Conclusion: The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study
may provide promising diagnostic methods for sepsis.