Bioinformatics Analysis of the Pathogenesis and Therapeutic Targets of Sepsis

Background/Aims: This study aimed to comprehensively characterize the pathogenesis of sepsis, screen related genes, and obtain new therapeutic targets. Methods: GSE28750 chip data were obtained from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by the significance analysis of microarrays. The Database for Annotation, Visualization and Integrated Discovery was used for a gene ontology analysis and metabolic pathway analysis. The Gene-Cloud of Biotechnology Information database was used for a gene network analysis. A co-expression analysis was performed using weighted gene correlation network analysis software and the VisANT website. Results: Compared with the control group, 2,457 DEGs were identified in the sepsis group, including 1,282 upregulated genes and 1,175 downregulated genes. These DEGs were mainly involved in immune response, cell differentiation, blood coagulation, and so on. In a pathway analysis, the core signaling pathways were involved mainly in substance metabolism, signal transduction, anti-infection, and other processes. A gene network analysis identified several core genes, including GNAI3, PIK3CB, MAPK14, and IL8. A co-expression network analysis also speculated about core genes such as GYG1, SERPINB1, SAMSN1, and ATP11B. Conclusion: Bioinformatics approaches were used to comprehensively evaluate the pathogenesis of sepsis and to identify potential therapeutic targets, providing a basis for the development of effective treatments. Funding Statement: The authors stated: "The study did not accept any funding." Declaration of Interests: There are no conflicts of interest in this study. Ethics Approval Statement: Not required. The authors stated: "This article does not contain any studies with patients or animals performed by any of the authors."