Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis

• Published in: International journal of molecular sciences Vol. 24; no. 3; p. 2591
• Main Authors: Fang, Changyin, Ma, Yongping
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.01.2023; MDPI
• Subjects: Antigens; Bioinformatics; Biomarkers; Blood; Computational Biology; Coronaviruses; COVID-19; COVID-19 - genetics; Cytokine storm; Datasets; differentially expressed genes; Disease; Drug interaction; drug molecule; Drugs; Functional analysis; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Gene set enrichment analysis; Genes; Humans; Immune response; Lymphocytes; Microorganisms; MicroRNAs - genetics; miRNA; Molecular modelling; Multiple organ dysfunction syndrome; Pathogenesis; Peripheral blood; Protein interaction; Protein Interaction Maps - genetics; Proteins; protein–protein interaction; Risk factors; SARS-CoV-2 - genetics; Sepsis; Sepsis - complications; Sepsis - genetics; Severe acute respiratory syndrome coronavirus 2; Signal Transduction - genetics; T cell receptors; Transcription Factors - genetics; COVID-19; protein–protein interaction; drug molecule; differentially expressed genes; sepsis
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24032591

Severe coronavirus disease 2019 (COVID-19) has led to a rapid increase in death rates all over the world. Sepsis is a life-threatening disease associated with a dysregulated host immune response. It has been shown that COVID-19 shares many similarities with sepsis in many aspects. However, the molecular mechanisms underlying sepsis and COVID-19 are not well understood. The aim of this study was to identify common transcriptional signatures, regulators, and pathways between COVID-19 and sepsis, which may provide a new direction for the treatment of COVID-19 and sepsis. First, COVID-19 blood gene expression profile (GSE179850) data and sepsis blood expression profile (GSE134347) data were obtained from GEO. Then, we intersected the differentially expressed genes (DEG) from these two datasets to obtain common DEGs. Finally, the common DEGs were used for functional enrichment analysis, transcription factor and miRNA prediction, pathway analysis, and candidate drug analysis. A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets. Protein-protein interactions (PPIs) were constructed using the STRING database. Subsequently, hub genes were identified based on PPI networks. In addition, we performed GO functional analysis and KEGG pathway analysis of common DEGs, and found a common association between sepsis and COVID-19. Finally, we identified transcription factor-gene interaction, DEGs-miRNA co-regulatory networks, and protein-drug interaction, respectively. Through ROC analysis, we identified 10 central hub genes as potential biomarkers. In this study, we identified SARS-CoV-2 infection as a high risk factor for sepsis. Our study may provide a potential therapeutic direction for the treatment of COVID-19 patients suffering from sepsis.