Identification of sepsis-related genes by integrating eQTL data with Mendelian randomization analysis

Background Sepsis is defined as a life-threatening organ dysfunction caused by a dysfunctional host response to infection and is associated with a high mortality. However, there is currently no effective treatment strategy for sepsis. Methods We obtained GSE263789, GSE54514 and GSE66099 from the Gene Expression Omnibus (GEO) database and selected differentially expressed genes (DEGs). We extracted expression quantitative trait loci (eQTL) as exposure and sepsis GWAS as outcome from the IEU Open GWAS database. MR analysis was used to assess causality between eQTL and sepsis. The overlapping genes of DEGs with significant eQTL were identified as key genes. Enrichment analysis and immune cell infiltration analysis were performed and the expression of key genes was verified in a validation cohort. Results The 18 genes were identified as sepsis-related key genes, including 11 up-regulated genes (SEMA4A, LRPAP1, FAM89B, TOMM40L, SLC22A15, MACF1, MCTP2, NTSR1, PNKD, ACTR10, CPNE3) and 7 down-regulated genes (IKZF3, TNFRSF25, HDC, HCP5, LYRM4, TFAM, RPS15A). Enrichment analyses showed that these key genes are mainly involved in biological processes related to immune and inflammatory response. Compared with healthy controls, the abundance of neutrophils and activated mast cells increased in the sepsis group. Most of the key genes are correlated with immune cells, including neutrophils, CD8 T cells, resting NK cells, plasma cells, memory B cells, and macrophage subtypes. Conclusion By combining bioinformatics and MR analysis, we identified key genes associated with sepsis, enhancing our understanding of the genetic pathogenesis of sepsis and providing new insights into therapeutic targets for sepsis.