Bioinformatics analysis identifies BST1 as a potential therapeutic target linked to neutrophil extracellular traps in...

Acute on chronic liver failure (ACLF) is a critical condition with a high mortality rate; however, the underlying mechanisms driving its progression remain poorly understood. Neutrophil extracellular traps (NETs), a recently discovered mechanism of cell death, may play a significant role in this process. Although NETs has been found in a variety of liver diseases, its specific mechanism in regulating the development of ACLF is not clear. Here, we sought to identify key NETs-related genes associate with ACLF. In this study, gene expression data from 24 whole blood samples, including 7 healthy donors and 17 ACLF patients, were downloaded from the Gene Expression Omnibus database. Various bioinformatics analyses, including Weighted Gene Co-expression Network Analysis (WGCNA) and CIBERSORT, were performed to further analyse and compare the differential genes between patients with acute on chronic liver failure and healthy groups. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the differential genes. Differentially expressed genes (DEGs) were analyzed against 69 known NETs-related genes, and a genetic diagnostic model for ACLF patients was established based on the analysis results. In addition, we used an unsupervised clustering method to assess immune infiltration and biological differences based on NETs-related genes. Finally, mice peripheral blood was collected for RT-qPCR verification. A total of 1694 DEGs were identified, and the biological functions and signaling pathways closely related to ACLF were monocyte differentiation and immune-metabolic processes. Twenty-seven expression modules were obtained by WGCNA, among which the ME pink and ME cyan module had the highest correlation with ACLF. The intersection of the results from both analyses with 69 NETs-related genes identified 13 key genes (FCAR, MMP9, ITGAM, DYSF, ALPL, MPO, MGAM, TLR8, BST1, ELANE, TLR4, TLR2, PADI4). Subsequently, CIBERSORT analysis revealed that immune cells, such as M0-type macrophages, activated memory CD4 T cells, and plasma cells, might play an important role in ACLF. Additionally, NETs-related genes were closely associated with immune infiltration. TLR4, BST1, MGAM, DYSF, and ALPL were significantly associated with immune cells. The results of RT-qPCR on mice peripheral blood showed that BST1 expression increased significantly. BST1 may be reliable marker for the diagnosis of ACLF patients, and these results may contribute to a better understanding of the underlying molecular mechanisms of ACLF.