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Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock.

ABSTRACT: 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.

SUBMITTER: Hu Y

PROVIDER: S-EPMC6929537 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock.

Hu Yingchun Y Cheng Lingxia L Zhong Wu W Chen Muhu M Zhang Qian Q

Medical science monitor : international medical journal of experimental and clinical research 20191215

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 ...[more]

PMID: 31838482

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Project description:BackgroundHerein, we applied bioinformatics methods to analyze the crosstalk between septic shock (SS) and venous thromboembolism (VTE), focusing on the correlation with immune infiltrating cells.MethodsExpression data were obtained from the Gene Expression Omnibus (GEO) database, including blood samples from SS patients (datasets GSE64457, GSE95233, and GSE57065) and VTE patients (GSE19151). We used the R package "limma" for differential expression analysis (p value<0.05,∣logFC∣≥1). Venn plots were generated to identify intersected differential genes between SS and VTE and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment analysis. The protein-protein interaction (PPI) network of intersected genes was constructed by Cytoscape software. The xCell analysis identified immune cells with significant changes in VTE and SS and correlated them with significant molecular pathways of crosstalk. Finally, we validated the mRNA expression of crosstalk genes by qPCR, while Matrix Metalloprotein-9 (MMP-9) protein levels were assessed through Western blotting (WB) and Immunohistochemistry (IHC) in human umbilical vein endothelial cells (HUVECs) and mice.ResultsIn the present study, we conducted a comparison between 88 patients with septic shock and 55 control subjects. Additionally, we compared 70 patients with venous thromboembolism to 63 control subjects. Twelve intersected genes and their corresponding three important molecular pathways were obtained: Metabolic, Estrogen, and FOXO signaling pathways. The resulting PPI network has 194 nodes and 388 edges. The immune microenvironment analysis of the two diseases showed that the infiltration levels of M2 macrophages and Class-switched memory B cells were correlated with the enrichment scores of metabolic, estrogen, and FOXO signaling pathways. Finally, qPCR confirmed that the expression of MMP9, S100A12, ARG1, SLPI, and ANXA3 mRNA in the SS with VTE group was significantly elevated. WB and IHC experiments revealed that MMP9 protein was significantly elevated in the experimental group.ConclusionMetabolic, estrogen, and FOXO pathways play important roles in both SS and VTE and are related to the immune cell microenvironment of M2 macrophages and Class-switched memory B cells. MMP9 shows promise as a biomarker for diagnosing sepsis with venous thrombosis and a potential molecular target for treating this patient population.

Dataset Information

Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock.

Publications

Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock.

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