Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis.

BACKGROUND

Sepsis, is a life-threatening syndrome triggered by infection. Bile acid metabolism may be involved in the pathogenesis of sepsis, the underlying association has not yet been elucidated. Thus, we aimed to screen for bile acid metabolism-related biomarkers of sepsis and discover the potential association.

METHODS

Sepsis-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) and bile acid metabolism-related differentially expressed genes (BAMRDEGs), then identified hub genes using protein-protein interaction (PPI) network analysis and CytoHubba algorithm. After GO/KEGG enrichment analysis, a sepsis risk prediction model based on key genes was subsequently constructed using support vector machine recursive feature elimination (SVM-RFE) machine learning and least absolute shrinkage and selection operator (LASSO) regression. CIBERSORTx analysis was performed to assess immune cell infiltration and its association with key genes. Finally, the transcriptional levels of key genes in sepsis samples were detected by Quantitative Real-time PCR (qRT-PCR).

RESULTS

9785 DEGs were identified, including 5138 upregulated and 4647 downregulated genes. Additionally, 25 hub genes were identified. Gene enrichment analysis indicated that the hub genes participate in multiple biological pathways. Key genes (ABCC2, PECR, EPHX2, PEX2, and AGXT) exert central roles in the development of sepsis, indicating the involvement of bile acid metabolism. Significant correlations existed between the expression of key BAMRDEGs and the levels of different immune cell types. qRT-PCR suggested significant up-regulation on ABCC2 and AGXT in sepsis samples versus controls.

CONCLUSION

This study revealed novel insights into the correlation between sepsis and bile acid metabolism, and identified 5 key genes involved in the development of sepsis, providing molecular targets and novel strategies for the diagnosis and treatment of sepsis.