Preliminary Screening of miRNA Disease Markers for Acute Myocardial and Cerebral Infarctions
10.12007/j.issn.0258-4646.2017.08.003
- VernacularTitle:急性心肌梗死和急性脑梗死miRNA疾病标志物的初步筛查
- Author:
Shouzhi DU
1
;
Bin DONG
;
Zhonghua QI
Author Information
1. 大连医科大学附属第二医院创伤急症中心
- Keywords:
acute myocardial infarction;
acute cerebral infarction;
miRNA;
disease marker
- From:
Journal of China Medical University
2017;46(8):681-685
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify a new miRNA combination that could be used as a diagnostic marker for acute myocardial infarction (AMI) or acute cerebral infarction (ACI).Methods We surveyed the literatures and databases and selected miRNAs with a high frequency in AMI or ACI as candidate genes.We collected peripheral blood samples from 13 patients with AMI,11 patients with ACI,and 20 healthy volunteers.RNA was extracted from these samples,and gene expression levels were determined by reverse transcription and real-time quantitative PCR.The differences in the expression of the candidate miRNAs were then analyzed.Results The frequencies of miRNA1,miRNA499a,and miRNA 133a were higher in the AMI group,whereas the frequencies of let7i,miRNA16,and miRNA223 were higher in the ACI group.These six miRNAs were significantly higher in the two disease groups than in the control group (P ＜ 0.01).The levels of miRNA1 and miRNA499a were 4.82-and 3.50-times higher in the AMI group,respectively (both P ＜ 0.01),when compared with the ACI group.The levels of let7i in the ACI group were 2.61-times higher than that in the AMI group (P ＜ 0.05).There were no significant differences in the expression levels of miRNA 133a,miRNA16,and miR-NA233 between the AMI and ACI groups (P ＞ 0.05).Conclusion The abnormal increase in six miRNAs in human peripheral blood could be used as a marker for the diagnosis of AMI or ACI.The expression levels of miRNA 1 and miRNA499a were 11-to 20-times higher than control levels,and the expression levels of let7i were 6-times higher than control levels,which could be used to predict the risk of AMI and ACI,respectively.
