Aberrant expression of miR-483-5p in patients with asymptomatic carotid artery stenosis and its predictive value for cerebrovascular event occurrence

MicroRNAs (miRNAs/miRs) may be used as novel promising diagnostic and prognostic biomarkers for various diseases, including asymptomatic carotid artery stenosis (ACAS). The present study aimed to investigate the abnormal expression of microRNA-483-5p (miR-483-5p) in patients with ACAS and to evaluate its diagnostic value for ACAS screening and its predictive value for cerebrovascular events. A total of 128 patients with ACAS and 76 healthy controls were included in the present study. The expression of miR-483-5p in serum was measured by reverse transcription-quantitative PCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of miR-483-5p in patients with ACAS. Kaplan-Meier curves were drawn and Cox regression analysis was used to determine the predictive value of miR-483-5p for cerebrovascular events in patients with ACAS. Serum miR-483-5p levels were significantly increased in patients with ACAS as compared with those in healthy controls. The expression of miR-483-5p was significantly associated with diabetes (P=0.011), dyslipidemia (P=0.047) and the degree of carotid stenosis (P=0.006) in patients with ACAS. In addition, the area under the ROC curve was 0.910, with a sensitivity of 80.5% and a specificity of 89.5% at the cutoff value of 0.705, indicating that serum miR-483-5p expression has a certain diagnostic value in patients with ACAS. Furthermore, the patients with high miR-483-5p expression had a higher proportion of cerebrovascular events than patients with low miR-483-5p levels (log-rank P=0.011) and miR-483-5p was an independent prognostic marker for predicting the occurrence of cerebrovascular events in patients with ACAS. The results indicated that miR-483-5p expression is significantly increased in patients with ACAS and that abnormal miR-483-5p expression may be a candidate biomarker for ACAS diagnosis and the prediction of cerebrovascular event occurrence.