Multiple datasets to explore the molecular mechanism of sepsis

Background: This study aimed to identify potential biomarkers, by means of bioinformatics, affecting the occurrence and development of septic shock. Methods: : The self-test data and differential genes in GSE131761 were screened, and the diagnostic model of sepsis was further constructed by Lasso regression to identify key genes. The stability of the model was verified by ROC curve. Finally, immune infiltration analysis, enrichment analysis, transcriptional regulation analysis and correlation analysis were performed. Results: : A total of 292 differential genes were screened out from the self-test data, 294 differential genes were screened out by GSE131761, Lasso regression was performed on the intersection genes of the two, a diagnostic model was constructed, and 5 genes were identified as biomarkers of septic shock. These 5 genes were SIGLEC10, VSTM1, GYPB, OPTN, and GIMAP7. The five key genes were strongly correlated with immune cells, and the ROC results showed that the five genes had good predictive performance on the occurrence and development of diseases. In addition, the key genes were strongly correlated with immune regulatory genes. Conclusion: In this study, a series of algorithms were used to identify five key genes that are associated with septic shock, which may become potential candidate targets for septic shock diagnosis and treatment. Trial registration ：Approval number：2019XE0149-1，Project name：Study on the relationship between TLRs signaling pathway and sepsis.