Identification of module genes and functional pathway analysis in septic shock subtypes by integrated bioinformatics analysis.

BACKGROUND The present study aimed to identify the module genes and key gene functions and biological pathways of septic shock (SS) through integrated bioinformatics analysis. METHODS In the study, we performed batch correction and principal component analysis on 282 SS samples and 79 normal control samples in three datasets, GSE26440, GSE95233 and GSE57065, to obtain a combined corrected gene expression matrix containing 21,654 transcripts. Patients with SS were then divided into three molecular subtypes according to sample subtyping analysis. RESULTS By analyzing the demographic characteristics of the different subtypes, we found no statistically significant differences in gender ratio and age composition among the three groups. Then, three subtypes of differentially expressed genes (DEGs) and specific upregulated DEGs (SDEGs) were identified by differential gene expression analysis. We found 7361 DEGs in the type I group, 5594 DEGs in the type II group, and 7159 DEGs in the type III group. There were 1698 SDEGs in the type I group, 2443 in the type II group, and 1831 in the type III group. In addition, we analyzed the correlation between the expression data of 5972 SDEGs in the three subtypes and the gender and age of 227 patients, constructed a weighted gene co-expression network, and identified 11 gene modules, among which the module with the highest correlation with gender ratio was MEgrey. The modules with the highest correlation with age composition were MEgrey60 and MElightyellow. Then, by analyzing the differences in module genes among different subgroups of SS, we obtained the differential expression of 11 module genes in four groups: type I, type II, type III and the control group. Finally, we analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of all module DEGs, and the GO function and KEGG pathway enrichment of different module genes were different. CONCLUSIONS Our findings aim to identify the specific genes and intrinsic molecular functional pathways of SS subtypes, as well as further explore the genetic and molecular pathophysiological mechanisms of SS.

[1]  Linjing Wu,et al.  Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation , 2022, Frontiers in Molecular Biosciences.

[2]  Yu Liang,et al.  Identification of Methylation-Regulated Differentially Expressed Genes and Related Pathways in Hepatocellular Carcinoma: A Study Based on TCGA Database and Bioinformatics Analysis , 2021, Frontiers in Oncology.

[3]  J. Werner,et al.  Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients , 2020, Cancers.

[4]  Kazuki Nishida,et al.  Impact of Blood Type O on Mortality of Sepsis Patients: A Multicenter Retrospective Observational Study , 2020, Diagnostics.

[5]  Fangwei Li,et al.  m6A RNA Methylation Regulators Participate in the Malignant Progression and Have Clinical Prognostic Value in Lung Adenocarcinoma , 2020, Frontiers in Genetics.

[6]  Teng Xu,et al.  Novel miRNA markers for the diagnosis and prognosis of endometrial cancer , 2020, Journal of cellular and molecular medicine.

[7]  Xin Xu,et al.  Identification of AUNIP as a candidate diagnostic and prognostic biomarker for oral squamous cell carcinoma , 2019, EBioMedicine.

[8]  Teng Zhao,et al.  Losmapimod Protected Epileptic Rats From Hippocampal Neuron Damage Through Inhibition of the MAPK Pathway , 2019, Front. Pharmacol..

[9]  S. Duan,et al.  Differentially methylated regions in patients with rheumatic heart disease and secondary pulmonary arterial hypertension , 2017, Experimental and therapeutic medicine.

[10]  Limin Xu,et al.  Early diagnosis of bacterial infection in patients with septicopyemia by laboratory analysis of PCT, CRP and IL-6 , 2017, Experimental and therapeutic medicine.

[11]  S. Mohapatra,et al.  Transcriptomic meta-analysis reveals up-regulation of gene expression functional in osteoclast differentiation in human septic shock , 2017, PloS one.

[12]  M. Wurfel,et al.  TNFAIP2 Inhibits Early TNFa-Induced NF-κB Signaling and Decreases Survival in Septic Shock Patients , 2015, Journal of Innate Immunity.

[13]  N. Osterrieder,et al.  Equid Herpesvirus Type 1 Activates Platelets , 2015, PloS one.

[14]  M. I. Sierra,et al.  Expansion on Stromal Cells Preserves the Undifferentiated State of Human Hematopoietic Stem Cells Despite Compromised Reconstitution Ability , 2013, PloS one.

[15]  S. Chun,et al.  The Novel Role of Platelet-Activating Factor in Protecting Mice against Lipopolysaccharide-Induced Endotoxic Shock , 2009, PloS one.

[16]  François Vincent,et al.  Norepinephrine weaning in septic shock patients by closed loop control based on fuzzy logic , 2008, Critical care.

[17]  T. Yoshikawa,et al.  Protective Effects of Urinary Trypsin Inhibitor on Systemic Inflammatory Response Induced by Lipopolysaccharide , 2008, Journal of clinical biochemistry and nutrition.