Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis

Abstract

Background:This work aimed to screen key biomarkers related to sepsis progression by bioinformatics analyses. Material/Methods:The microarray datasets of blood and neutrophils from patients with sepsis or septic shock were downloaded from Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) from 4 groups (sepsis versus normal blood samples; septic shock versus normal blood samples; sepsis neutrophils versus normal controls and septic shock neutrophils versus controls) were respectively identified followed by functional analyses. Subsequently, protein-protein network was constructed, and key functional sub-modules were extracted. Finally, receiver operating characteristic analysis was conducted to evaluate diagnostic values of key genes. Results:There were 2082 DEGs between blood samples of sepsis patients and controls, 2079 DEGs between blood samples of septic shock patients and healthy individuals, 6590 DEGs between neutrophils from sepsis and controls, and 1056 DEGs between neutrophils from septic shock patients and normal controls. Functional analysis showed that numerous DEGs were significantly enriched in ribosome-related pathway, cell cycle, and neutrophil activation involved in immune response. In addition, TRIM25 and MYC acted as hub genes in protein-protein interaction (PPI) analyses of DEGs from microarray datasets of blood samples. Moreover, MYC (AUC=0.912) and TRIM25 (AUC=0.843) had great diagnostic values for discriminating septic shock blood samples and normal controls. RNF4 was a hub gene from PPI analyses based on datasets from neutrophils and RNF4 (AUC=0.909) was capable of distinguishing neutrophil samples from septic shock samples and controls. Conclusions:Our findings identified several key genes and pathways related to sepsis development.

Gene correlation network analysis to identify regulatory factors in sepsis

“…First, sepsis is shown to have a dysregulated immune response highlighted by the upregulation of the black module involving biological functions such as myeloid leukocyte mediated immunity, neutrophil [4,30,31]. However, previous studies mostly analyzed high-throughput data at individual gene level involving differential expression analysis followed by functional pathway enrichment [4,15,31,32]. In neonate sepsis, Meng and colleagues identified 7 hub genes in key pathways.…”

“…First, sepsis is shown to have a dysregulated immune response highlighted by the upregulation of the black module involving biological functions such as myeloid leukocyte mediated immunity, neutrophil mediated immunity, leukocyte degranulation and myeloid cell activation involved in immune response. Immune dysfunction has long been recognized as an important mediator of sepsis [ 4 , 30 , 31 ]. However, previous studies mostly analyzed high-throughput data at individual gene level involving differential expression analysis followed by functional pathway enrichment [ 4 , 15 , 31 , 32 ].…”

“…Immune dysfunction has long been recognized as an important mediator of sepsis [ 4 , 30 , 31 ]. However, previous studies mostly analyzed high-throughput data at individual gene level involving differential expression analysis followed by functional pathway enrichment [ 4 , 15 , 31 , 32 ]. In neonate sepsis, Meng and colleagues identified 7 hub genes in key pathways.…”

“…Several studies have used transcriptome analysis to investigate potential biological pathways regulating the pathogenesis of sepsis [4][5][6][7][8]. These studies were performed by differential gene expression analysis, followed by enrichment analyses to established functional pathways.…”

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

“…Another approach to precision medicine is to make use of the transcriptome data, which has been widely used in other critical care settings such as sepsis. [113][114][115] However, the omics have not been fully explored for the implementation of precision medicine for ARDS patients. Identification of subphenotypes based on transcriptome could provide additional information for accurate classifi-cation.…”