Objective. This study is aimed at exploring the underlying molecular mechanisms of ST-segment elevation myocardial infarction (STEMI) and provides potential clinical prognostic biomarkers for STEMI. Methods. The GSE60993 dataset was downloaded from the GEO database, and the differentially expressed genes (DEGs) between STEMI and control groups were screened. Enrichment analysis of the DEGs was subsequently performed using the DAVID database. A protein–protein interaction network was constructed, and hub genes were identified. The hub genes in patients were then validated by quantitative reverse transcription-PCR. Furthermore, hub gene-miRNA interactions were evaluated using the miRTarBase database. Finally, patient data on classical cardiovascular risk factors were collected, and plasma microRNA-146a (miR-146a) levels were detected. An individualized nomogram was constructed based on multivariate Cox regression analysis. Results. A total of 239 DEGs were identified between the STEMI and control groups. Expression of S100A12 and miR-146a was significantly upregulated in STEMI samples compared with controls. STEMI patients with high levels of miR-146a had a higher risk of major adverse cardiovascular events (MACEs) than those with low levels of miR-146a (log-rank P = 0.034 ). Multivariate Cox regression analysis identified five statistically significant variables, including age, hypertension, diabetes mellitus, white blood cells, and miR-146a. A nomogram was constructed to estimate the likelihood of a MACE at one, two, and three years after STEMI. Conclusion. The incidence of MACEs in STEMI patients expressing high levels of miR-146a was significantly greater than in those expressing low levels. MicroRNA-146a can serve as a biomarker for adverse prognosis of STEMI and might function in its pathogenesis by targeting S100A12, which may exert its role via an inflammatory response. In addition, our study presents a valid and practical model to assess the probability of MACEs within three years of STEMI.
Objective To investigate whether there is a connection between the plasma expression level of miR-146a and both the severity of coronary lesion and clinical prognosis in patients with unstable angina pectoris (UA). Methods: 100 unstable angina pectoris(UA group) and 100 healthy controls (Control group) were selected to detect the plasma miRNA-146a expression level. To assess the coronary lesion severity in UA patients by Gensini score, analyze the correlation between miR-146a expression level and the degree of coronary artery stenosis in UA patients. The incidence of major cardiovascular adverse events (MACE) were followed up for 48 months after hospitalization and discharge in UA patients. Using the median grouping method to divide the miR-146a expression level in 100 UA patients into high and low expression groups, analyzing the incidence of MACE in patients with different miRNA-146a expression level by the Kaplan-Meier method. Results: The plasma expression level of miR-146a in the UA group was 1.8 times higher than in the control group (Z=6.970, P <0.001), and was correlated with the severity of coronary lesion; the high expression level was associated with a higher Gensini score (P<0.05). Patients with high miR-146a expression level had a significantly higher incidence of MACE compared to those with low miR-146a expression level (Log-rank: P=0.004). Conclusion: The plasma miR-146a expression level of UA patients was correlated with the severity of coronary lesion, and patients with higher miR-146a expression level had a poor clinical prognosis than those with lower expression level.a pectoris (UA group) and 100 healthy controls (Control group) were selected to detect the plasma miRNA-146a expression level. To assess the coronary lesion severity in UA patients by Gensini score, analyze the correlation between miR-146a expression level and the degree of coronary artery stenosis in UA patients. The incidence of major cardiovascular adverse events (MACE) were followed up for 48 months after hospitalization and discharge in UA patients. Using the median grouping
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