The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights

Abstract

Acute Myocardial infarction (AMI) continues to be a severe deadly disease with a high case-fatality rate and great psychological and economic costs. Despite the development of new therapies, early diagnosis and treatment optimization still are problematical. Here we applied the integrated bioinformatics strategy to screen potential new diagnosis biomarkers for AMI based on the inflammation-related cGAS-STING pathway. By integrating differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and cGAS-STING gene set from high-throughput RNA sequencing, we obtained a total of 11 candidate genes from 914 DEGs, which enriched mainly for inflammatory and immune response pathways. Further refining them in machine learning algorithms, we identified core genes with diagnostic values. Subsequent test shows the AUC values of PFKFB3 and TLR2 both were more than 0.7 and their promising diagnostic values were verified. Based on the immune infiltration analysis, expression of PFKFB3 and TLR2 were very significantly associated with a large number of immune cells, such as monocytes, neutrophils and dendritic cells. As for single cell sequencing analysis, they were mainly found expressed on macrophages. Overall, we show that PFKFB3 and TLR2 are possible biomarkers of AMI which have not been reported so far. Thus they may open a path for new diagnostics and personalized therapy. Validation in independent populations is needed.