Identification and validation of prognostic signature and immune infiltrate characteristics for acute myocardial infarction based on neutrophil extracellular traps

Abstract Background Coronary artery blockage can lead to acute myocardial infarction (AMI), characterized by myocardial ischemia, necrosis, and compromised cardiac function. Recent studies highlight the significance of neutrophil extracellular traps (NETs) in AMI's progression and prognosis. This research delves into the expression patterns of NETs-related genes (NRGs) in AMI cases. It aims to explore the association between immune infiltration linked to NRGs and AMI, as well as their potential as prognostic markers. Methods Data for AMI was sourced from the GSE59867 dataset in the Gene Expression Omnibus (GEO) database, complemented by NETs-related genes from existing literature. Differential expression analysis of these genes (DE-NRGs) was conducted between control and AMI samples. This was followed by functional enrichment analysis of the DE-NRGs. The degree of immune infiltration was quantified via ImmuncellAI, facilitating the analysis of correlations between critical genes and neutrophils. A LASSO algorithm-based model was developed using NRGs to predict relevant features. Subsequently, a protein-protein interaction (PPI) network analysis of the DE-NRGs was performed to identify central hub genes and potential high-correlation predictors. The validity of these analyses was confirmed through the development of an AMI rat model and subsequent validation of key predictors via Western blotting. Results In the bioinformatics examination of the GSE59867 dataset, a notable variance in gene expression was observed between AMI and normal samples. Analysis of immune infiltration in selected DE-NRGs revealed a significant link with Neutrophils, monocytes, and NK-T cells (p < 0.05). The LASSO algorithm identified nine potential predictors: CXCL 8, IL 1 B, CAT, PRKCA, HDAC 6, HDAC 9, G0S2, CD93, and GP1BA. When these were cross-referenced with the top 50 hub genes from the PPI network, four genes (CXCL 8, IL 1 B, PRKCA, HDAC 6) emerged as significant predictors for AMI risk classification. Notably, CXCL 8 exhibited a strong positive correlation with neutrophils (R = 0.35, p < 0.01), whereas PRCKA showed a negative association (R = -0.20, p < 0.01). Additional external validation affirmed the classifier's high accuracy (AUC = 0.82). Western blot analyses post-acute MI revealed a marked increase in citH 3 and CXCL 8 expression levels (p < 0.05). Conclusions A predictive model for AMI was developed based on NETs-associated genes, focusing on their correlation with immune infiltration. Four DE-NRGs, namely CXCL 8, IL 1 B, PRKCA, and HDAC 6, demonstrated high predictive accuracy for myocardial infarction risk. This model not only sheds light on the inflammatory injury in AMI but also suggests targeted therapeutic approaches for clinical management of AMI.