Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes

Background Acute Coronary Syndrome (ACS) threatens human health worldwide. Early noninvasive assessment of the severity of ACS is helpful for its screening, treatment and management. Neutrophil extracellular trapping nets (NETs) are networks produced by neutrophils which released after stimulation to capture and eliminate microorganisms. NETs have recently been found to have an important role in ACS. The aim of this study was to investigate NETs-associated genes (NRGs) during ACS and to identify their association with ACS severity in different populations. Methods Differential gene analysis and WGCNA analysis were performed using the data set in GEO database, and the genes obtained from the two analyses were intersected with NRGs to the key genes involved in regulating ACS. The resulting genes were subjected to protein-protein interaction network analysis and functional enrichment analysis. ACS and control patients were selected as the validation cohort, Elisa was used to detect the expression of key genes, univariate logistic regression analysis was performed, ROC curve was plotted, sensitivity, specificity, and optimal cut-off value (cut-off value) were calculated. Multivariate logistic regression analysis and subgroup analysis were performed according to the results of the difference analysis. Results In this study, CCL4, CXCL2, IL1β, IL8, CXCL1 and TNFAIP3 were selected as key NRGs in ACS by intersecting DEGs, WGCNA and NRGs. A total of 318 clinical samples (228 ACS and 90 controls) were collected as the validation cohort, and Elisa results showed that CCL4, CXCL2, IL1β, IL8, and CXCL1 was higher in ACS group, while TNFAIP3 expression was lower. Univariate logistic regression analysis showed that all six continuous variables were statistically significant for ACS. ROC curves showed that high expression of CCL4, CXCL2, IL1β, IL8, CXCL1 and low expression of TNFAIP3 were all associated with an increased risk of ACS. And IL1β, CXCL1, and TNFAIP3 were better predictive of ACS (AUC > 0.8). Multivariate logistics analysis of the overall and subgroup populations showed that these six NRGs were independent predictors of ACS in the overall population, but these six indicators showed different predictive effects in different subgroup populations. CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable. Conclunsion The key variables selected by NRGs can predict the severity of ACS, which provide some reference for the screening and treatment of ACS.