exploring the molecular mechanism of sepsis comprehensively based on multi-omics analysis

Background: Sepsis remains a life-threatening disease with a high mortality rate that causes millions of deaths worldwide every year. However, the molecular mechanism remains incompletely unknown. Bioinformatics analysis has been widely used in microarray data to identify biomarkers of sepsis and perform subsequent analysis to identify potential therapeutic interventions. Methods: The GSE175453, GSE185263 and GSE28750 datasets were obtained from Gene Expression Omnibus (GEO) database and eQTL data comes from the eQTLGen consortium database. Limma package and ClusterProfiler analysis were used to identify differentially expressed genes and explore the functional correlation of these genes. Functional analysis and immune infiltration analysis were used to investigate the biological characteristics and immune cell enrichment in sepsis patients. Then the correlation between pyroptosis-related genes and immune cells was analyzed and the diagnostic value of the selected genes was assessed using the receiver operating characteristic curve. Mendelian randomization (MR) analysis was first conducted to investigate the hub genes related to sepsis. Sensitivity analyses were applied to validate the reliability of MR results. Then artificial neural network models were constructed for diagnosis and immune cell infiltration analysis was performed to explore the potential molecular mechanisms. Cmap drug prediction was made to discover the therapeutic effect on sepsis. Results: Our investigation revealed 204 differentially expressed genes (DEGs) in sepsis datasets including 124 up-regulated genes and 80 down-regulated genes. The MR analysis showed six hub genes (CA1, CD96, IL18R1, ORM1, S100P, SLC26A8) may be associated with a high or low risk of sepsis. Analyzes suggested that key genes are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment. We constructed an artificial neural network model with high diagnostic and studied the specific signaling pathways enriched in 6 key genes performance. Furthermore, by transcriptional regulation analysis, corresponding transcription factors of key genes were displayed and Cmap drug prediction showed expression profiles perturbed by drugs such as Entecavir, Linsitinib, SDZ-205-557, and Triacsin C had a significant negative correlation with the disease which may have therapeutic effective. Conclusion: Our investigation revealed six key genes related to sepsis, displayed the signaling pathways enriched in the key genes to explore the molecular mechanisms of sepsis, and offered potential therapeutic targets or biomarkers, shed light on the immune microenvironment by immune infiltration analysis, and proposed prospective alternative pathways for targeted therapeutic interventions by multi-omics analysis initially.