Evaluation of exosomal miRNAs as potential diagnostic biomarkers for acute myocardial infarction using next-generation sequencing

使用下一代测序评估外泌体 miRNA 作为急性心肌梗死的潜在诊断生物标志物

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作者:Mei Guo, Rui Li, Linfeng Yang, Qianhua Zhu, Mo Han, Zhichao Chen, Fengying Ruan, Yongxian Yuan, Zhenni Liu, Binbin Huang, Mingzhou Bai, Hongqi Wang, Chao Zhang, Chong Tang

Background

Acute myocardial infarction (AMI) is one of the most common global causes of death. Although considerable progress has been made in AMI diagnosis, there remains an urgent need for novel diagnostic biomarkers for its prevention and treatment. Functional exosomal microRNAs (miRNAs) are recognized as potential biomarkers in many diseases. This study's

Conclusions

Nanoparticle diameter and the 18 miRNAs may serve as simple and accessible fingerprints for early AMI diagnosis.

Methods

Exosomes from the plasma of 26 coronary artery disease (CAD) patients, 55 AMI patients, and 37 healthy controls were isolated and characterized by transmission electron microcopy (TEM), western blotting, and nanoparticle tracking analysis (NTA). The miRNAs were purified from exosomes, and unique molecular identifier (UMI) small RNA sequencing was performed. The random forest (RF) model was trained to predict potential biomarkers.

Results

NTA demonstrated that nanoparticle concentration did not change after AMI, while nanoparticle size distribution significantly decreased. The CAD and AMI groups' miRNA expression profiles significantly differed from the healthy group's profile. The RF classifier could be used to distinguish the healthy group from the AMI group, but could not be used to distinguish the CAD group from the other groups, which caused a high classification error rate. Eighteen miRNAs were selected as biomarkers based on their RF classifier significance. The diagnostic accuracy of 18 miRNAs was evaluated using AUC values of 0.93, 0.87, and 0.75 to detect healthy controls, AMI, and CAD, respectively. Conclusions: Nanoparticle diameter and the 18 miRNAs may serve as simple and accessible fingerprints for early AMI diagnosis.

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