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Updated: Sep 10, 2025
Digital PCR for Quantifying Circulating MicroRNAs in Acute Myocardial Infarction and Cardiovascular Disease
Published on: July 3, 2018
Differential diagnosis of acute myocardial infarction based on plasma Exosomal MicroRNA
Peng Zhou1,2,3, Jia Zhang1,2,3, Xiangjun Wu1
1Binzhou People's Hospital, Shandong First Medical University, Binzhou, 256600, PR China.
Object:
The differentiation of acute myocardial infarction (AMI) has long been a challenging problem in clinical diagnosis and forensic identification. Recent studies have shown that microRNAs (miRNAs) in exosomes are involved in the development and progression of AMI. results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.
Method:
In this study, exosomal miRNAs in plasma associated with the pathogenesis of AMI was explored and AMI identification model based on these miRNAs were established using machine learning technology.
Result:
Following the analysis of differentially expressed miRNAs in plasma-derived exosomes, the expression levels of 36 miRNAs increase with the passage of time, including miR-3473, miR-504, miR-490-5p, miR-218a-2-3p, and miR-760-3p, showed an increasing trend over time in the plasma exosomes of AMI rats. Based on machine learning techniques, miR-3473, miR-504, miR-490-5p, miR-218a-2-3p were used to construct a model for recognizing early AMI. The precision of the AMI identification model reached 0.955.
Conclusion:
The results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.
Insights
Plasma exosomal microRNAs (miRNAs) show promise as novel biomarkers for early acute myocardial infarction (AMI) recognition. Machine learning models utilizing specific miRNAs achieved high precision in identifying AMI.
Area of Science:
- Biochemistry
- Molecular Biology
- Cardiovascular Research
Background:
- Acute myocardial infarction (AMI) diagnosis remains challenging for clinical and forensic purposes.
- Exosomes and their microRNAs (miRNAs) are increasingly recognized for their role in AMI development and progression.
Purpose of the Study:
- To explore plasma exosomal miRNAs in AMI pathogenesis.
- To establish a machine learning-based model for early AMI identification using exosomal miRNAs.
Main Methods:
- Analysis of differentially expressed miRNAs in plasma-derived exosomes.
- Development of an AMI identification model utilizing machine learning algorithms.
- Selection of specific miRNAs (miR-3473, miR-504, miR-490-5p, miR-218a-2-3p) for model construction.
Main Results:
- Identified 36 miRNAs with expression levels increasing over time in AMI rat plasma exosomes.
- Constructed an AMI identification model using four key miRNAs: miR-3473, miR-504, miR-490-5p, and miR-218a-2-3p.
- Achieved a high precision of 0.955 for the developed AMI identification model.
Conclusions:
- Plasma exosomal miRNAs serve as effective novel biomarkers for early AMI detection.
- The established machine learning model demonstrates significant potential for accurate AMI recognition.
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