BACKGROUND: Septic shock occurs when sepsis is associated with critically low blood pressure, and has a high mortality rate. This study aimed to undertake a bioinformatics analysis of gene expression profiles for risk prediction in septic shock.

MATERIAL AND METHODS: Two good quality datasets associated with septic shock were downloaded from the Gene Expression Omnibus (GEO) database, GSE64457 and GSE57065. Patients with septic shock had both sepsis and hypotension, and a normal control group was included. The differentially expressed genes (DEGs) were identified using OmicShare tools based on R. Functional enrichment of DEGs was analyzed using DAVID. The protein-protein interaction (PPI) network was established using STRING. Survival curves of key genes were constructed using GraphPad Prism version 7.0. Each putative central gene was analyzed by receiver operating characteristic (ROC) curves using MedCalc statistical software.

RESULTS: GSE64457 and GSE57065 included 130 RNA samples derived from whole blood from 97 patients with septic shock and 33 healthy volunteers to obtain 975 DEGs, 455 of which were significantly down-regulated and 520 were significantly upregulated (P<0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified significantly enriched DEGs in four signaling pathways, MAPK, TNF, HIF-1, and insulin. Six genes, WDR82, ASH1L, NCOA1, TPR, SF1, and CREBBP in the center of the PPI network were associated with septic shock, according to survival curve and ROC analysis.

CONCLUSIONS: Bioinformatics analysis of gene expression profiles identified four signaling pathways and six genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in septic shock.

Keywords: Genes, vif, Sepsis, Shock, Computational Biology, Databases, Genetic, Gene Expression Regulation, gene ontology, Gene Regulatory Networks, genetic testing, Protein Interaction Mapping, Protein Interaction Maps, ROC Curve, Shock, Septic, Software