Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

ABSTRACT

Acute myocardial infarction (AMI) was considered a fatal disease resulting in high morbidity and mortality; platelet activation or aggregation plays a critical role in participating in the pathogenesis of AMI. The current study aimed to reveal the underlying mechanisms of platelets in the confrontation of AMI and potential biomarkers that separate AMI from other cardiovascular diseases and healthy people with bioinformatic strategies. Immunity analysis revealed that the neutrophil was significantly decreased in patients with SCAD compared with patients with ST-segment elevation myocardial infarction (STEMI) or healthy controls; monocytes and neutrophils showed potential in distinguishing patients with STEMI from patients with SCAD. Six differentially expressed genes (DEGs) showed great performances in differentiating STEMI patients from SCAD patients with AUC greater than 0.9. Correlation analysis showed that these six DEGs were significantly positively correlated with neutrophils; three genes were negatively correlated with monocytes. Weighted gene co-expression network analysis (WGCNA) found that module ‘royalblue’ had the highest correlation with STEMI; genes in STEMI-related module were enriched in cell–cell interactions, blood vessels’ biological processes, and peroxisome proliferator-activated receptor (PPAR) signaling pathway; four genes (FN1, CD34, LPL, and WWTR1) represented the capability of identifying patients with STEMI from healthy controls and patients with SCAD; two genes (ARG1 and NAMPTL) were considered as novel biomarkers for identifying STEMI from SCAD; FN1 represented the potential as a novel biomarker for STEMI. Our findings indicated that the distribution of neutrophils could be considered as a potential molecular trait for separating patients with STEMI from SCAD.

Graphical Abstract

KEYWORDS:

• Neutrophils
• STEMI
• scad
• arg1
• namptl
• fn1

Acknowledgements

None.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Disclosure statement

The authors declare there are no competing interests.

Author contributions

Study design: Yun Xie

Data collection: Yi Wang

Data analysis: Linjun Zhao

Interpretation of data: Fang Wang

Draft manuscript: Yun Xie

Review manuscript: Jinyan Fang

Additional information

Funding

This work was supported by grants from Zhejiang Medical and Health Science and Technology Plan Project (2021KY242) and Traditional Chinese Medical science and technology plan of Zhejiang Province (2019ZB099);Traditional Chinese Medical science and technology plan of Zhejiang Province [2019ZB099].