Dataset Information

Interaction between microRNA expression and classical risk factors in the risk of coronary heart disease.

ABSTRACT: The aim of this study was to identify the synergistic effect of microRNA expression with classical risk factors of coronary heart disease (CHD) and to explore their diagnostic value for coronary stenotic lesions in subjects with CHD. Plasma samples were obtained from 66 subjects with CHD and from 58 control individuals. A quantitative reverse-transcription PCR (RT-qPCR) assay was conducted to confirm the relative expressions of the known CHD-related miRNAs. The severity of coronary atherosclerosis was based on the Gensini scoring system. The expression of miR-125b in plasma of the CHD group was lower than that of the non-CHD group (0.14 ± 0.09 vs. 0.18 ± 0.10, p = 0.055), and the miR-125b levels significantly decreased following an increasing Gensini score (P = 0.037). Spearman correlation analyses indicated the Gensini score was negatively associated with miR-125b (r = -0.215, p = 0.017). Of all the miRNAs, miR-125b showed the lowest AUC (0.405; 95% CI: 0.305 ~ 0.506, p = 0.070). We found several synergistic effects between miR-125b and classical risk factors, such as age, sex, CR, FBG and HDL-C; the proportion of CHD attributable to the interaction of miR-125b and age was as high as 80%. Therefore, miR-125b was shown to play an important role in individual's susceptibility to developing CHD.

SUBMITTER: Ding XQ

PROVIDER: S-EPMC4597355 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundRecent studies suggest that classical coronary risk factors play a significant role in the pathogenesis of coronary artery disease. Our study aims to explore the interaction of circRNA with classical coronary risk factors in coronary atherosclerotic disease.MethodCombined analysis of RNA sequencing results from coronary segments and peripheral blood mononuclear cells of patients with coronary atherosclerotic disease was employed to identify critical circRNAs. Competing endogenous RNA networks were constructed by miRanda-3.3a and TargetScan7.0. The relative expression quantity of circRNA in peripheral blood mononuclear cells was determined by qRT-PCR in a large cohort including 256 patients and 49 controls. Spearman's correlation test, receiver operating characteristic curve analysis, multivariable logistic regression analysis, one-way analysis of variance, and crossover analysis were performed.ResultsA total of 34 circRNAs were entered into our study, hsa_circRPRD1A, hsa_circHERPUD2, hsa_circLMBR1, and hsa_circDHTKD1 were selected for further investigation. A circRNA-miRNA-mRNA network is composed of 20 microRNAs and 66 mRNAs. The expression of hsa_circRPRD1A (P = 0.004) and hsa_circHERPUD2 (P = 0.003) were significantly down-regulated in patients with coronary artery disease compared to controls. The area under the curve of hsa_circRPRD1A and hsa_circHERPUD2 is 0.689 and 0.662, respectively. Univariate and multivariable logistic regression analyses identified hsa_circRPRD1A (OR = 0.613, 95%CI:0.380-0.987, P = 0.044) as a protective factor for coronary artery disease. Based on the additive model, crossover analysis demonstrated that there was an antagonistic interaction between the expression of hsa_circHERPUD2 and alcohol consumption in subjects with coronary artery disease.ConclusionOur findings imply that hsa_circRPRD1A and hsa_circHERPUD2 could be used as biomarkers for the diagnosis of coronary artery disease and provide epidemiological support for the interactions between circRNAs and classical coronary risk factors.

Project description:BACKGROUND:Coronary heart disease (CHD) is a complex disease caused by multi-factors and a major threat to human health. Circular RNAs (circRNAs) have critical roles in various biological processes and diseases. This study explores the independent role of circRNAs and their interaction with environmental factors in CHD. METHODS:A case-control study was conducted from March 2015 to September 2017 in Fuzhou, China. A total of 585 CHD patients and 585 gender- and age-matched healthy controls were enrolled. Questionnaire survey, health examination and molecular biology laboratory testing were conducted. Microarray technology and quantitative real-time polymerase chain reaction (PCR) were used to profile the expression levels of circRNAs. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to determine the diagnostic cut-offs. Multivariate logistic regression and multiplicative analysis were used to analyse the effects of environmental factors and hsa_circ_0008507, hsa_circ_0001946, hsa_circ_0000284 and hsa_circ_0125589 on CHD. RESULTS:The expression profile of circRNAs showed that 3423 circRNAs were differentially expressed at P < 0.05, but none pass multiple testing correction. qRT-PCR further confirmed the expression levels of hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 in peripheral blood leukocytes in CHD cases were higher than those in non-CHD subjects (All p < 0.05). Hsa_circ_0008507 (OR = 1.29; 95% CI: 1.11-1.50), hsa_circ_0001946 (OR = 1.20; 95% CI: 1.01-1.42) and hsa_circ_0000284 (OR = 2.05; 95% CI: 1.32-3.19) were independent risk factors for CHD after controlling other common environmental risk factors. The AUC for hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 was 0.75, 0.71 and 0.68, respectively. Compared with non-smoking individuals with low hsa_circ_0008507 expression, the smokers with high hsa_circ_0008507 expression showed the highest magnitude of OR in CHD risk. Additionally, a statistically significant multiplicative interaction was found between hsa_circ_0008507 and smoking for CHD. CONCLUSIONS:Hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 were closely related to the occurrence and development of CHD. The combination of smoking and high hsa_circ_0008507 expression causes the occurrence and development of CHD.

Dataset Information

Interaction between microRNA expression and classical risk factors in the risk of coronary heart disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets