Circulating pro-angiogenic micro-ribonucleic acid in patients with coronary heart disease

Circulating pro-angiogenic micro-ribonucleic acid in patients with coronary heart disease Abstract OBJECTIVES Our goal was to evaluate the expressions of 14 selected pro-angiogenic micro-ribonucleic acids in patients with coronary heart disease (CHD) and healthy controls (HCs) and to assess the correlations of those micro-ribonucleic acids with risk and severity of CHD. METHODS In the exploration stage, 20 patients with CHD were enrolled; in the validation stage, 102 patients with CHD and 92 age- and gender-matched HCs with the same eligibility of those in the exploration stage were recruited. Blood samples were collected from all participants, and plasma levels of micro-ribonucleic acids were measured by the quantitative polymerase chain reaction method. RESULTS In the exploration stage, the expression of miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 in patients with CHD was down-regulated compared with that of HCs. In the validation stage, miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 levels decreased remarkably in patients with CHD compared with the HCs. Plasma levels of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 were independent prediction factors for CHD. The combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD with an area under the curve of 0.756. Additionally, plasma levels of miR-126, miR-210 and miR-378 correlated negatively with Gensini scores. CONCLUSIONS Circulating miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 could serve as novel, promising biomarkers for risk and severity of CHD. Additionally, miR-126, miR-210 and miR-378 were negatively associated with Gensini scores. Circulating, Pro-angiogenic, Micro-ribonucleic acid, Coronary heart disease INTRODUCTION Coronary heart disease (CHD), one of the most prevalent cardiovascular diseases (CVDs), is one of the leading causes of mortality and morbidity worldwide [1]. Cardiovascular magnetic resonance imaging and single-photon emission computed tomography for the diagnosis of patients with CHD are generally adopted in clinical practice, whereas the unstable quality of the images still makes them not always optimal [2]. Although angiography is accurate in detecting CHD, its invasiveness and high level of radiation exposure still prevent its use in many patients suspected of having CHD [3]. Thus, a less invasive, more accurate diagnostic procedure is badly needed. Micro-ribonucleic acids (miRNAs) are a class of 19–25 nucleotide noncoding RNAs that negatively regulate gene expression by triggering either translation repression or RNA degradation at the post-transcriptional level [4]. Emerging numbers of observations and experiments revealed that miRNAs play crucial roles in the physiology and pathology in both animal and human organisms. In particular, miRNAs such as miR-125b were found to be negatively associated with Gensini scores [1, 5, 6]. Angiogenesis, a dominant process in the pathogenesis of CHD, could be a therapeutic target in patients with CHD through promoting blood vessel growth, improving tissue perfusion and enhancing muscle recovery [7, 8]. Recently, several miRNAs were shown to be pro-angiogenic; however, evidence of the diagnostic value of these pro-angiogenic miRNAs for CHD is still scarce. Therefore, the goal of our study was to evaluate the expressions of 14 selected pro-angiogenic miRNAs in patients with CHD and in healthy controls (HCs) and to assess the correlations of these miRNAs with the risk and severity of CHD. MATERIALS AND METHODS Participants This study comprised 2 stages: the exploration stage and the validation stage, as shown in Fig. 1. In the exploration stage, 20 patients with angiographically documented CHD at Xingtai People’s Hospital from June 2015 to August 2015 were recruited in this study. The exclusion criteria for patients with CHD were (i) impaired ejection fraction; (ii) heart failure; (iii) unstable CHD and (iv) acute myocardial injury. Twenty age- and gender-matched HCs without any evidence of CHD were also enrolled during the same period with the following exclusions: history of leucopenia, thrombocytopenia, severe infection, CVD, cerebrovascular disease, malignant disease and hepatic or renal dysfunction. Additionally, the HCs were not examined angiographically because they had no indications for angiography. In the validation stage, 102 patients with CHD and 92 age- and gender-matched HCs with the same eligibility criteria of those in the exploration stage were enrolled from June 2015 to May 2016, including 20 patients and 20 HCs in the exploration stage. The dataset for the validation stage was analysed after the exploration stage was completed. This study was approved by the ethics committee of Xingtai People’s Hospital, and all participants provided written informed consent. Figure 1: View largeDownload slide Study flow. CHD: coronary heart disease; miRNA: micro-ribonucleic acid. Figure 1: View largeDownload slide Study flow. CHD: coronary heart disease; miRNA: micro-ribonucleic acid. Evaluation of coronary artery disease severity using the Gensini score Disease severity was assessed using the Gensini scoring system, which was calculated according to the reduction in the diameter of the lumen and the roentgenographic appearance of concentric lesions and eccentric plaques [9]. Reductions in lumen diameter of 25%, 50%, 75%, 90%, 99% and complete occlusion were scored as 1, 2, 4, 8, 16 and 32, respectively. Moreover, the scores were multiplied by each principal vascular segment according to the functional significance of the myocardial area supplied by that segment: the left main coronary artery, ×5; the proximal segment of the left anterior descending coronary artery, ×2.5; the proximal segment of the circumflex artery, ×2.5; the mid-segment of the left anterior descending coronary artery, ×1.5; the right coronary artery, the distal segment of the left anterior descending coronary artery, the posterolateral artery and the obtuse marginal artery, ×1 and all others, ×0.5. Sample collection Blood samples were collected from participants and stored in ethylenediaminetetraacetic acid tubes, 2 h after the temperature reached room temperature, the blood samples were subsequently centrifuged at 3000 r/min for 10 min at 4°C. The upper plasma fraction was subsequently obtained and further centrifuged at 12 000 r/min for 15 min at 4°C. The plasma was then collected and stored at −80°C. Detection of micro-ribonucleic acids by quantitative polymerase chain reaction Total RNA was extracted from the plasma using TRIzol LS Reagent (Ambion, Carlsbad, CA, USA), and the concentration and purity were measured using a spectrophotometer. RNA then underwent reverse transcription using the PrimerScript Real-time reagent kit (TaKaRa, Otsu, Shiga, Japan), and relative quantitative measurements of selected miRNAs expressions were performed using SYBR Premix Ex TaqTM II (TaKaRa, Otsu, Shiga, Japan) strictly according to the manufacturer’s instructions. U6 was used as an internal reference, and the expressions of selected miRNAs were calculated by the 2−△△t method. Statistics The SPSS 21.0 program was used for statistical analysis in this study. Data were presented as mean ± standard deviation, median and 25th–75th percentile value or count and percentage. The expression of miRNAs in patients with CHD and in the HCs in the exploration stage was compared using the Mann–Whitney test, and the expression of miRNAs in patients with CHD and in HCs in the validation stage was compared using the t-test. The characteristics that were classified as variables were compared in patients with CHD and HCs using the χ2 test. The Pearson test was used to assess the correlations of 6 differentially expressed miRNAs levels with Gensini scores. Univariable and multivariable logistic regression analyses were performed to evaluate the risk factors for CHD. The receiver operating characteristic (ROC) curve was used to assess the diagnostic value of selected miRNAs for CHD. P-value <0.05 was considered significant. RESULTS Characteristics of patients with coronary heart disease and healthy controls in the exploration stage In the exploration stage, the mean age of patients with CHD and HCs was 59.6 ± 9.7 years and 57.2 ± 8.5, respectively (P = 0.410) (Table 1). There were 16 women and 4 men in the CHD group, and 15 women and 5 men in the HC group (P = 0.705). In addition, the mean body mass index of the patients with CHD and the HCs was 24.0 ± 2.8 kg/m2 and 23.2 ± 2.9 kg/m2, respectively (P = 0.380). However, the number of participants with hypertension in the patients with CHD was higher than that among the HCs [15 (75%) vs 6 (30%), P = 0.004]. Three (15%) patients with CHD and 2 (10%) HCs had diabetes (P = 0.677). The 2 groups were compared using the t-test and the χ2 test. Other clinical characteristics and laboratory indexes are listed in Table 1. Table 1: Characteristics of patients with coronary heart disease and healthy controls in the exploration stage Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or the χ2 test. P-value <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HCs: healthy controls; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Table 1: Characteristics of patients with coronary heart disease and healthy controls in the exploration stage Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or the χ2 test. P-value <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HCs: healthy controls; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Differentially expressed micro-ribonucleic acids in the exploration stage In the exploration stage, the expression of 14 pro-angiogenic miRNAs in plasma was determined in 20 patients with CHD and in 20 HCs using the quantitative real-time polymerase chain reaction method. As shown in Table 2, the expression of miR-126 (P = 0.043), miR-17-5p (P = 0.019), miR-19a (P = 0.043), miR-92a (P = 0.047), miR-210 (P = 0.048) and miR-378 (P = 0.033) in patients with CHD was down-regulated compared with that in the HCs. The comparison between the 2 groups were compared using the Mann–Whitney test. The mean differences in the expressions of miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 in patients with CHD and HCs were 3.11, 3.76, 3.29, 3.86, 3.12 and 2.84, respectively, which indicated that the differences were biologically significant. These 6 differentially expressed miRNAs were subsequently selected to be demonstrated in the validation stage. The full sequences of the 14 miRNAs are listed in Supplementary Material, Table S1. Table 2: Fourteen circulating pro-angiogenic miRNAs in patients with coronary heart disease and in healthy controls in the exploration stage Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  The boldface values stand for values with statistical significance. Data were presented as mean, SD, median and 95% CI. Comparison was determined by the Mann–Whitney test. P-value <0.05 was considered significant. CHD: coronary heart disease; CI: confidence interval; HC: healthy control; miRNA: micro-ribonucleic acid; SD: standard deviation. Table 2: Fourteen circulating pro-angiogenic miRNAs in patients with coronary heart disease and in healthy controls in the exploration stage Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  The boldface values stand for values with statistical significance. Data were presented as mean, SD, median and 95% CI. Comparison was determined by the Mann–Whitney test. P-value <0.05 was considered significant. CHD: coronary heart disease; CI: confidence interval; HC: healthy control; miRNA: micro-ribonucleic acid; SD: standard deviation. Characteristics of patients with coronary heart disease and the healthy controls in the validation stage As shown in Table 3, the mean ages were 60.2 ± 11.4 years and 57.9 ± 14.8 years in patients with CHD and the HCs (P = 0.231), respectively. There were 81 women and 21 men in the patients with CHD group and 66 women and 26 men in the HC group (P = 0.213). The mean body mass index of patients with CHD and the HCs was 24.2 ± 3.7 kg/m2 and 23.6 ± 3.5 kg/m2, respectively (P = 0.249). The number of participants who had hypertension [73 (72%) vs 38 (41%), P < 0.001], diabetes [22 (22%) vs 10 (11%), P = 0.045] and a history of smoking [49 (48%) vs 31 (34%), P = 0.043] in patients with CHD was greater than that in the HCs. The 2 groups were compared using the t-test and the χ2 test. Other information about clinical characteristics, disease history and laboratory indexes is listed in Table 3. Table 3: Characteristics of patients with coronary heart disease and healthy controls in the validation stage Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or χ2 test. P-value  <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HC: healthy control; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Table 3: Characteristics of patients with coronary heart disease and healthy controls in the validation stage Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or χ2 test. P-value  <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HC: healthy control; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Expression of selected micro-ribonucleic acids in the validation stage In the validation stage, 6 selected differentially expressed miRNAs were further measured by the quantitative real-time polymerase chain reaction in a large population of 102 patients with CHD and 92 HCs. The levels of miR-126 (P = 0.001), miR-17-5p (P = 0.008), miR-92a (P = 0.003), miR-210 (P = 0.002) and miR-378 (P = 0.040) were remarkably decreased in patients with CHD compared with HCs (Fig. 2). However, no difference in the miR-19a plasma level was observed between patients with CHD and HCs (P = 0.091). The 2 groups were compared using the t-test. Figure 2: View largeDownload slide Plasma levels of 6 differentially expressed micro-ribonucleic acids from patients with CHD and HCs in the validation stage. (A) Plasma level of miR-126 in patients with CHD and in HCs. (B) Plasma level of miR-17-5p in patients with CHD and in HCs. (C) Plasma level of miR-19a in patients with CHD and in HCs. (D) Plasma level of miR-92a in patients with CHD and in HCs. (E) Plasma level of miR-210 in patients with CHD and in HCs. (F) Plasma level of miR-378 in patients with CHD and in HCs. The 2 groups were compared using the t-test. P-value <0.05 was considered significant. CHD: coronary heart disease; HC: healthy control. Figure 2: View largeDownload slide Plasma levels of 6 differentially expressed micro-ribonucleic acids from patients with CHD and HCs in the validation stage. (A) Plasma level of miR-126 in patients with CHD and in HCs. (B) Plasma level of miR-17-5p in patients with CHD and in HCs. (C) Plasma level of miR-19a in patients with CHD and in HCs. (D) Plasma level of miR-92a in patients with CHD and in HCs. (E) Plasma level of miR-210 in patients with CHD and in HCs. (F) Plasma level of miR-378 in patients with CHD and in HCs. The 2 groups were compared using the t-test. P-value <0.05 was considered significant. CHD: coronary heart disease; HC: healthy control. To evaluate the levels of the 6 differentially expressed miRNAs in the exploration stage for predicting the risk of CHD, univariable and multivariable logistic regression analyses were performed in the validation stage. As listed in Table 4, plasma levels of miR-126 (P = 0.001), miR-17-5p (P = 0.008), miR-92a (P = 0.003), miR-210 (P = 0.002) and miR-378 (P = 0.039) were protective factors for CHD, whereas expression of miR-19a (P = 0.085) in plasma was not a predictive factor for CHD. Multivariate regression analysis was performed using a stepwise method, which confirmed that plasma levels of miR-126 (P = 0.001), miR-17-5p (P = 0.013), miR-92a (P = 0.016), miR-210 (P < 0.001) and miR-378 (P = 0.019) were independent predictive factors for CHD. Table 4: Logistic regression analysis for 6 differentially expressed micro-ribonucleic acids in the validation stage   Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981    Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981  The boldface values stand for values with statistical significance. Data were presented as P-value, OR and 95% CI. Significance was determined by univariable and multivariable logistic regression analysis. P-value <0.05 was considered significant. CI: confidence interval; OR: odds ratio. Table 4: Logistic regression analysis for 6 differentially expressed micro-ribonucleic acids in the validation stage   Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981    Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981  The boldface values stand for values with statistical significance. Data were presented as P-value, OR and 95% CI. Significance was determined by univariable and multivariable logistic regression analysis. P-value <0.05 was considered significant. CI: confidence interval; OR: odds ratio. Diagnostic value of selected micro-ribonucleic acids for coronary heart disease To evaluate the independent predictive value of miRNAs for the diagnosis of CHD, we conducted an ROC curve analysis. The area under the curve (AUC) of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 for predicting CHD was 0.641 [95% confidence interval (CI) 0.564–0.719], 0.609 (95% CI 0.526–0.692), 0.622 (95% CI 0.541–0.702), 0.617 (95% CI 0.537–0.698) and 0.574 (95% CI 0.492–0.656), respectively. When we combined the 5 selected miRNAs, the AUC was 0.756 (95% CI 0.687–0.725) with a sensitivity of 84.3% and a specificity of 60.9% at the best cut-off point, which indicated a good diagnostic value for CHD (Fig. 3). Figure 3: View largeDownload slide Receiver operating characteristic curves of 5 differentially expressed micro-ribonucleic acids in the validation stage for coronary artery disease. The analysis was determined by receiver operating characteristic curve analysis. AUC: area under the curve; CI: confidence interval. Figure 3: View largeDownload slide Receiver operating characteristic curves of 5 differentially expressed micro-ribonucleic acids in the validation stage for coronary artery disease. The analysis was determined by receiver operating characteristic curve analysis. AUC: area under the curve; CI: confidence interval. Correlation of selected micro-ribonucleic acids with Gensini scores To detect the association of selected miRNAs with CHD severity, we analysed the association of plasma levels of selected miRNAs with Gensini scores. The plasma levels of miR-126 (r = −0.343, P < 0.001), miR-210 (r = −0.384, P < 0.001) and miR-378 (r = −0.224, P = 0.024) were negatively correlated with Gensini scores. However, no association of miR-17-5p (r = −0.087, P = 0.386), miR-19a (r = −0.082, P = 0.414) and miR-92a (r = 0.066, P = 0.510) with Gensini scores was found (Fig. 4). The correlations were also analysed using the Pearson test. Figure 4: View largeDownload slide Correlations of 6 differentially expressed micro-ribonucleic acids plasma levels compared with Gensini scores. (A) Correlation of miR-126 plasma level with Gensini scores. (B) Correlation of miR-17-5p plasma level with Gensini scores. (C) Correlation of miR-19a plasma level with Gensini scores. (D) Correlation of miR-92a plasma level with Gensini scores. (E) Correlation of miR-210 plasma level with Gensini scores. (F) Correlation of miR-378 plasma level with Gensini scores. The Pearson test was used to assess the correlations of Gensini scores with 6 differentially expressed micro-ribonucleic acids levels. P-value <0.05 was considered significant. Figure 4: View largeDownload slide Correlations of 6 differentially expressed micro-ribonucleic acids plasma levels compared with Gensini scores. (A) Correlation of miR-126 plasma level with Gensini scores. (B) Correlation of miR-17-5p plasma level with Gensini scores. (C) Correlation of miR-19a plasma level with Gensini scores. (D) Correlation of miR-92a plasma level with Gensini scores. (E) Correlation of miR-210 plasma level with Gensini scores. (F) Correlation of miR-378 plasma level with Gensini scores. The Pearson test was used to assess the correlations of Gensini scores with 6 differentially expressed micro-ribonucleic acids levels. P-value <0.05 was considered significant. Analysis in the validation stage including patients who were not included in the exploration stage As presented in Supplementary Material, Fig. S1, in the validation stage including patients who were not included in the exploration stage, the expressions of miR-126 (P < 0.001), miR-17-5p (P = 0.042), miR-92a (P = 0.017), miR-210 (P = 0.017) and miR-378 (P < 0.001) were down-regulated in patients with CHD compared with those in the HCs. However, the expression of miR-19a was similar in patients with CHD and HCs (P = 0.150). The 2 groups were compared using the t-test. In addition, univariable and multivariable logistic regression analyses were performed to evaluate the predictive value of 6 differentially expressed miRNAs for CHD. In univariable logistic regression analysis, miR-126 (P = 0.001), miR-17-5p (P = 0.039), miR-210 (P = 0.017) and miR-378 (P < 0.001) were protective factors for CHD (Supplementary Material, Table S2), whereas miR-19a (P = 0.137) and miR-92a (P = 0.075) were not correlated with the risk of CHD. Subsequently, the multivariable logistic regression analysis using a stepwise model indicated that miR-126 (P = 0.001), miR-210 (P = 0.002) and miR-378 (P < 0.001) were independently associated with CHD. The ROC curve analysis conducted in the validation stage only included patients who were not included in the exploration stage, which indicated that the AUC of miR-126, miR-210 and miR-378 was 0.666 (95% CI 0.581–0.751), 0.596 (95% CI 0.504–0.688) and 0.706 (95% CI 0.626–0.787), respectively. When we combined the 3 miRNAs, we found that the AUC was 0.792 (95% CI 0.722–0.862) with a sensitivity of 73.2% and a specificity of 73.6% at the best cut-off point (Supplementary Material, Fig. S2). The Pearson test was used to correlate the 6 differentially expressed miRNAs with Gensini scores. As presented in Supplementary Material, Fig. S3, the expressions of miR-126 (P < 0.001), miR-17-5p (P = 0.042), miR-19a (P = 0.150), miR-92a (P = 0.017), miR-210 (P = 0.027) and miR-378 (P = 0.895) were not correlated with Gensini score. DISCUSSION Patients with CHD have a high risk for heart attacks and strokes, which are predominant causes of CHD deaths [10]. Thus, a rapid, accurate diagnostic procedure to ensure early, efficient interventions is badly needed. miRNAs were reported to be associated with several CVDs, such as acute myocardial infarctions [11]. We evaluated 14 selected miRNAs to explore their potential roles in predicting the risk and severity of CHD. We found that (i) in the exploration stage, miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 remarkably declined in patients with CHD compared with HCs. In the validation stage, only miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 levels were decreased in patients with CHD. Univariable and multivariable logistic regression showed that those miRNAs that were differentially expressed in the validation stage were independent protective factors for CHD. (ii) ROC curves indicated that the combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD; (iii) expressions of miR-126, miR-210 and miR-378 in plasma were negatively correlated with Gensini scores. Numerous studies have shown the angiogenic-related functions of miRNAs, some of which were pro-angiogenic and some of which were anti-angiogenic [12, 13]. MiR-126, located on chromosome 9q34.3, was shown to be richly expressed in endothelial cells [14]. In a study conducted on zebrafish, miR-126 was shown to mediate the endothelial cells by regulating the vascular endothelial growth factor responses, and the knockdown of miR-126 resulted in a loss of vascular integrity and haemorrhage in embryonic development [15]. Wang et al. [16] also discovered that miR-126 managed vascular integrity and angiogenesis in mutant animals by mediating growth factors such as vascular endothelial growth factor and fibroblast growth factor. miR-17-5p, another angiogenesis-related miRNA, was also reported to modulate angiogenesis by mediating the level of the antiangiogenic factor tissue inhibitor of metalloproteinase 1 in mice [17]. miR-92a, the angiogenesis of which is controversial, could suppress the angiogenic capabilities of mesenchymal stromal cells by downregulating the hepatocyte growth factor level [18]; and pretreatment with miR-92a strengthens capillary tube formation of human umbilical vein endothelial cells under oxidative stress [19]. Although the role of miR-210 is relatively clear, Yang et al. [20] identified miR-210 as a pro-angiogenesis miRNA due to its function of enhancing cancer angiogenesis through targeting fibroblast growth factor receptor-like 1 in patients with hepatocellular carcinoma. Moreover, in the study of Bakirtzi et al. [21], colitis and intestinal angiogenesis were promoted by neurotensin through Hif-1α-miR-210 signalling. As reported in a previous study, miR-378 enhances cell migration, invasion and tumour angiogenesis in brain metastases of non-small-cell lung cancer [22]. Similarly, miR-378 was found to play a pro-angiogenic role in tumours by targeting SuFu and Fus-1 levels [23]. In our study, miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 plasma levels decreased in patients with CHD and were factors for predicting lower risk of CHD, which might result from their angiogenic function validated by prior studies, whereas no difference was found in plasma levels of miR-19a in patients with CHD and in HCs. The severity of CHD can be evaluated using single-photon emission computed tomography with a semiconductor gamma camera using thallium-201, which assesses the level of ischaemia and the Gensini score [24]. To evaluate the severity of CHD, we used the Gensini score, which is a scoring system widely used in clinical practice that includes the percentage of stenosis and the location of the coronary artery stenosis. Also, miR-126, miR-210 and miR-378 were negatively correlated with Gensini scores, which might result from the fact that these miRNAs were shown to participate in angiogenesis among multiple diseases including CHD [14–23]. Besides, the correlations of miR-126, miR-210 and miR-378 with Gensini score were relatively weak, which might result from the fact that some of the associations might not be linear, which may result in the relatively low correlation coefficients. In addition, previous researchers who investigated the correlation of plasma miRNA expressions with the Gensini score also reported relatively low correlation coefficients. For instance, Ding et al. [6] reported that the correlation coefficient of plasma miR-125b expression with the Gensini score of patients with CHD was −0.215 with a P-value of 0.017. Other studies showed that miRNAs are dysregulated and play crucial roles in the pathogenesis of non-coronary ischaemic tissue in other diseases and that ischaemia is one of the most critical clinical features of patients with CHD. Bijkerk et al. [25] showed that miR-126 could ameliorate ischaemia/reperfusion injury by enhancing vascular integrity. Platelet-derived miR-92a decreases the levels of cysteine protease inhibitor cystatin C in patients with Type II diabetes with lower limb ischaemia [26]. Additionally, miR-17-5p induced by p53 protects against ischaemia–reperfusion injury in the kidney by targeting the death receptor 6 [27]. A number of studies reported the diagnostic value of miRNAs in CVD or cardiovascular-related diseases. They showed that circulating miR-92a-3p, miR-126, miR-143, miR-145 and miR-223 may be involved in the pathogenesis of CVD and could serve as potential biomarkers for diagnosis and prognosis [28–30]. We first discovered that the combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD. The result might provide an option for the early diagnosis for CHD and improve the prognosis of patients with CHD. However, the exact mechanisms of these miRNAs in CHD are still unclear and were not investigated in our study. The sample size in this study was relatively small; thus, a study with a large sample size that measures the detailed mechanisms should be conducted in the future. CONCLUSION In conclusion, our study showed that circulating miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 could serve as novel, promising biomarkers for the risk and severity of CHD. In addition, miR-126, miR-210 and miR-378 were negatively associated with Gensini scores. SUPPLEMENTARY MATERIAL Supplementary material is available at ICVTS online. Conflict of interest: none declared. REFERENCES 1 Sayed AS, Xia K, Salma U, Yang T, Peng J. Diagnosis, prognosis and therapeutic role of circulating miRNAs in cardiovascular diseases. Heart Lung Circ  2014; 23: 503– 10. Google Scholar CrossRef Search ADS PubMed  2 Greenwood JP, Maredia N, Younger JF, Brown JM, Nixon J, Everett CC et al.   Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet  2012; 379: 453– 60. Google Scholar CrossRef Search ADS PubMed  3 SCOT-HEART investigators. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet  2015; 385: 2383– 91. CrossRef Search ADS PubMed  4 Plasterk RH. Micro RNAs in animal development. Cell  2006; 124: 877– 81. Google Scholar CrossRef Search ADS PubMed  5 Urbich C, Kuehbacher A, Dimmeler S. Role of microRNAs in vascular diseases, inflammation, and angiogenesis. Cardiovasc Res  2008; 79: 581– 8. Google Scholar CrossRef Search ADS PubMed  6 Ding XQ, Ge PC, Liu Z, Jia H, Chen X, An FH et al.   Interaction between microRNA expression and classical risk factors in the risk of coronary heart disease. Sci Rep  2015; 5: 14925. Google Scholar CrossRef Search ADS PubMed  7 Ferrara N, Alitalo K. Clinical applications of angiogenic growth factors and their inhibitors. Nat Med  1999; 5: 1359– 64. Google Scholar CrossRef Search ADS PubMed  8 Yla-Herttuala S, Rissanen TT, Vajanto I, Hartikainen J. Vascular endothelial growth factors: biology and current status of clinical applications in cardiovascular medicine. J Am Coll Cardiol  2007; 49: 1015– 26. Google Scholar CrossRef Search ADS PubMed  9 Gensini GG. A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol  1983; 51: 606. Google Scholar CrossRef Search ADS PubMed  10 Who Cardiovascular Disease. http://www.Who.Int/topics/cardiovascular_diseases/en/ (14 March 2011, date last accessed). 11 Peng L, Chun-Guang Q, Bei-Fang L, Xue-Zhi D, Zi-Hao W, Yun-Fu L et al.   Clinical impact of circulating miR-133, miR-1291 and miR-663b in plasma of patients with acute myocardial infarction. Diagn Pathol  2014; 9: 89. Google Scholar CrossRef Search ADS PubMed  12 Wu F, Yang Z, Li G. Role of specific microRNAs for endothelial function and angiogenesis. Biochem Biophys Res Commun  2009; 386: 549– 53. Google Scholar CrossRef Search ADS PubMed  13 Jin F, Xing J. Circulating pro-angiogenic and anti-angiogenic microRNA expressions in patients with acute ischemic stroke and their association with disease severity. Neurol Sci  2017; 38: 2015– 23. Google Scholar CrossRef Search ADS PubMed  14 Parker LH, Schmidt M, Jin SW, Gray AM, Beis D, Pham T et al.   The endothelial-cell-derived secreted factor Egfl7 regulates vascular tube formation. Nature  2004; 428: 754– 8. Google Scholar CrossRef Search ADS PubMed  15 Fish JE, Santoro MM, Morton SU, Yu S, Yeh RF, Wythe JD et al.   miR-126 regulates angiogenic signaling and vascular integrity. Dev Cell  2008; 15: 272– 84. Google Scholar CrossRef Search ADS PubMed  16 Wang S, Aurora AB, Johnson BA, Qi X, McAnally J, Hill JA et al.   The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis. Dev Cell  2008; 15: 261– 71. Google Scholar CrossRef Search ADS PubMed  17 Otsuka M, Zheng M, Hayashi M, Lee JD, Yoshino O, Lin S et al.   Impaired microRNA processing causes corpus luteum insufficiency and infertility in mice. J Clin Invest  2008; 118: 1944– 54. Google Scholar CrossRef Search ADS PubMed  18 Kalinina N, Klink G, Glukhanyuk E, Lopatina T, Efimenko A, Akopyan Z et al.   miR-92a regulates angiogenic activity of adipose-derived mesenchymal stromal cells. Exp Cell Res  2015; 339: 61– 6. Google Scholar CrossRef Search ADS PubMed  19 Zhang L, Zhou M, Qin G, Weintraub NL, Tang Y. MiR-92a regulates viability and angiogenesis of endothelial cells under oxidative stress. Biochem Biophys Res Commun  2014; 446: 952– 8. Google Scholar CrossRef Search ADS PubMed  20 Yang Y, Zhang J, Xia T, Li G, Tian T, Wang M et al.   MicroRNA-210 promotes cancer angiogenesis by targeting fibroblast growth factor receptor-like 1 in hepatocellular carcinoma. Oncol Rep  2016; 36: 2553– 62. Google Scholar CrossRef Search ADS PubMed  21 Bakirtzi K, Law IK, Xue X, Iliopoulos D, Shah YM, Pothoulakis C. Neurotensin promotes the development of colitis and intestinal angiogenesis via Hif-1α-miR-210 signaling. J Immunol  2016; 196: 4311– 21. Google Scholar CrossRef Search ADS PubMed  22 Chen LT, Xu SD, Xu H, Zhang JF, Ning JF, Wang SF. MicroRNA-378 is associated with non-small cell lung cancer brain metastasis by promoting cell migration, invasion and tumor angiogenesis. Med Oncol  2012; 29: 1673– 80. Google Scholar CrossRef Search ADS PubMed  23 Lee DY, Deng Z, Wang CH, Yang BB. MicroRNA-378 promotes cell survival, tumor growth, and angiogenesis by targeting SuFu and Fus-1 expression. Proc Natl Acad Sci USA  2007; 104: 20350– 5. Google Scholar CrossRef Search ADS PubMed  24 Shiraishi S, Sakamoto F, Tsuda N, Yoshida M, Tomiguchi S, Utsunomiya D et al.   Prediction of left main or 3-vessel disease using myocardial perfusion reserve on dynamic thallium-201 single-photon emission computed tomography with a semiconductor gamma camera. Circ J  2015; 79: 623– 31. Google Scholar CrossRef Search ADS PubMed  25 Bijkerk R, van Solingen C, de Boer HC, van der Pol P, Khairoun M, de Bruin RG et al.   Hematopoietic microRNA-126 protects against renal ischemia/reperfusion injury by promoting vascular integrity. J Am Soc Nephrol  2014; 25: 1710– 22. Google Scholar CrossRef Search ADS PubMed  26 Zhang Y, Guan Q, Jin X. Platelet-derived miR-92a downregulates cysteine protease inhibitor cystatin C in type II diabetic lower limb ischemia. Exp Ther Med  2015; 9: 2257– 62. Google Scholar CrossRef Search ADS PubMed  27 Hao J, Wei Q, Mei S, Li L, Su Y, Mei C et al.   Induction of microRNA-17-5p by p53 protects against renal ischemia-reperfusion injury by targeting death receptor 6. Kidney Int  2017; 91: 106– 18. Google Scholar CrossRef Search ADS PubMed  28 Rong X, Jia L, Hong L, Pan L, Xue X, Zhang C et al.   Serum miR-92a-3p as a new potential biomarker for diagnosis of kawasaki disease with coronary artery lesions. J Cardiovasc Transl Res  2017; 10: 1– 8. Google Scholar CrossRef Search ADS PubMed  29 Massy ZA, Metzinger-Le Meuth V, Metzinger L. MicroRNAs are associated with uremic toxicity, cardiovascular calcification, and disease. Contrib Nephrol  2017; 189: 160– 8. Google Scholar CrossRef Search ADS PubMed  30 Cavarretta E, Frati G. MicroRNAs in coronary heart disease: ready to enter the clinical arena? Biomed Res Int  2016; 2016: 2150763. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Interactive CardioVascular and Thoracic Surgery Oxford University Press

Circulating pro-angiogenic micro-ribonucleic acid in patients with coronary heart disease

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
ISSN
1569-9293
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1569-9285
D.O.I.
10.1093/icvts/ivy058
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Abstract

Abstract OBJECTIVES Our goal was to evaluate the expressions of 14 selected pro-angiogenic micro-ribonucleic acids in patients with coronary heart disease (CHD) and healthy controls (HCs) and to assess the correlations of those micro-ribonucleic acids with risk and severity of CHD. METHODS In the exploration stage, 20 patients with CHD were enrolled; in the validation stage, 102 patients with CHD and 92 age- and gender-matched HCs with the same eligibility of those in the exploration stage were recruited. Blood samples were collected from all participants, and plasma levels of micro-ribonucleic acids were measured by the quantitative polymerase chain reaction method. RESULTS In the exploration stage, the expression of miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 in patients with CHD was down-regulated compared with that of HCs. In the validation stage, miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 levels decreased remarkably in patients with CHD compared with the HCs. Plasma levels of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 were independent prediction factors for CHD. The combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD with an area under the curve of 0.756. Additionally, plasma levels of miR-126, miR-210 and miR-378 correlated negatively with Gensini scores. CONCLUSIONS Circulating miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 could serve as novel, promising biomarkers for risk and severity of CHD. Additionally, miR-126, miR-210 and miR-378 were negatively associated with Gensini scores. Circulating, Pro-angiogenic, Micro-ribonucleic acid, Coronary heart disease INTRODUCTION Coronary heart disease (CHD), one of the most prevalent cardiovascular diseases (CVDs), is one of the leading causes of mortality and morbidity worldwide [1]. Cardiovascular magnetic resonance imaging and single-photon emission computed tomography for the diagnosis of patients with CHD are generally adopted in clinical practice, whereas the unstable quality of the images still makes them not always optimal [2]. Although angiography is accurate in detecting CHD, its invasiveness and high level of radiation exposure still prevent its use in many patients suspected of having CHD [3]. Thus, a less invasive, more accurate diagnostic procedure is badly needed. Micro-ribonucleic acids (miRNAs) are a class of 19–25 nucleotide noncoding RNAs that negatively regulate gene expression by triggering either translation repression or RNA degradation at the post-transcriptional level [4]. Emerging numbers of observations and experiments revealed that miRNAs play crucial roles in the physiology and pathology in both animal and human organisms. In particular, miRNAs such as miR-125b were found to be negatively associated with Gensini scores [1, 5, 6]. Angiogenesis, a dominant process in the pathogenesis of CHD, could be a therapeutic target in patients with CHD through promoting blood vessel growth, improving tissue perfusion and enhancing muscle recovery [7, 8]. Recently, several miRNAs were shown to be pro-angiogenic; however, evidence of the diagnostic value of these pro-angiogenic miRNAs for CHD is still scarce. Therefore, the goal of our study was to evaluate the expressions of 14 selected pro-angiogenic miRNAs in patients with CHD and in healthy controls (HCs) and to assess the correlations of these miRNAs with the risk and severity of CHD. MATERIALS AND METHODS Participants This study comprised 2 stages: the exploration stage and the validation stage, as shown in Fig. 1. In the exploration stage, 20 patients with angiographically documented CHD at Xingtai People’s Hospital from June 2015 to August 2015 were recruited in this study. The exclusion criteria for patients with CHD were (i) impaired ejection fraction; (ii) heart failure; (iii) unstable CHD and (iv) acute myocardial injury. Twenty age- and gender-matched HCs without any evidence of CHD were also enrolled during the same period with the following exclusions: history of leucopenia, thrombocytopenia, severe infection, CVD, cerebrovascular disease, malignant disease and hepatic or renal dysfunction. Additionally, the HCs were not examined angiographically because they had no indications for angiography. In the validation stage, 102 patients with CHD and 92 age- and gender-matched HCs with the same eligibility criteria of those in the exploration stage were enrolled from June 2015 to May 2016, including 20 patients and 20 HCs in the exploration stage. The dataset for the validation stage was analysed after the exploration stage was completed. This study was approved by the ethics committee of Xingtai People’s Hospital, and all participants provided written informed consent. Figure 1: View largeDownload slide Study flow. CHD: coronary heart disease; miRNA: micro-ribonucleic acid. Figure 1: View largeDownload slide Study flow. CHD: coronary heart disease; miRNA: micro-ribonucleic acid. Evaluation of coronary artery disease severity using the Gensini score Disease severity was assessed using the Gensini scoring system, which was calculated according to the reduction in the diameter of the lumen and the roentgenographic appearance of concentric lesions and eccentric plaques [9]. Reductions in lumen diameter of 25%, 50%, 75%, 90%, 99% and complete occlusion were scored as 1, 2, 4, 8, 16 and 32, respectively. Moreover, the scores were multiplied by each principal vascular segment according to the functional significance of the myocardial area supplied by that segment: the left main coronary artery, ×5; the proximal segment of the left anterior descending coronary artery, ×2.5; the proximal segment of the circumflex artery, ×2.5; the mid-segment of the left anterior descending coronary artery, ×1.5; the right coronary artery, the distal segment of the left anterior descending coronary artery, the posterolateral artery and the obtuse marginal artery, ×1 and all others, ×0.5. Sample collection Blood samples were collected from participants and stored in ethylenediaminetetraacetic acid tubes, 2 h after the temperature reached room temperature, the blood samples were subsequently centrifuged at 3000 r/min for 10 min at 4°C. The upper plasma fraction was subsequently obtained and further centrifuged at 12 000 r/min for 15 min at 4°C. The plasma was then collected and stored at −80°C. Detection of micro-ribonucleic acids by quantitative polymerase chain reaction Total RNA was extracted from the plasma using TRIzol LS Reagent (Ambion, Carlsbad, CA, USA), and the concentration and purity were measured using a spectrophotometer. RNA then underwent reverse transcription using the PrimerScript Real-time reagent kit (TaKaRa, Otsu, Shiga, Japan), and relative quantitative measurements of selected miRNAs expressions were performed using SYBR Premix Ex TaqTM II (TaKaRa, Otsu, Shiga, Japan) strictly according to the manufacturer’s instructions. U6 was used as an internal reference, and the expressions of selected miRNAs were calculated by the 2−△△t method. Statistics The SPSS 21.0 program was used for statistical analysis in this study. Data were presented as mean ± standard deviation, median and 25th–75th percentile value or count and percentage. The expression of miRNAs in patients with CHD and in the HCs in the exploration stage was compared using the Mann–Whitney test, and the expression of miRNAs in patients with CHD and in HCs in the validation stage was compared using the t-test. The characteristics that were classified as variables were compared in patients with CHD and HCs using the χ2 test. The Pearson test was used to assess the correlations of 6 differentially expressed miRNAs levels with Gensini scores. Univariable and multivariable logistic regression analyses were performed to evaluate the risk factors for CHD. The receiver operating characteristic (ROC) curve was used to assess the diagnostic value of selected miRNAs for CHD. P-value <0.05 was considered significant. RESULTS Characteristics of patients with coronary heart disease and healthy controls in the exploration stage In the exploration stage, the mean age of patients with CHD and HCs was 59.6 ± 9.7 years and 57.2 ± 8.5, respectively (P = 0.410) (Table 1). There were 16 women and 4 men in the CHD group, and 15 women and 5 men in the HC group (P = 0.705). In addition, the mean body mass index of the patients with CHD and the HCs was 24.0 ± 2.8 kg/m2 and 23.2 ± 2.9 kg/m2, respectively (P = 0.380). However, the number of participants with hypertension in the patients with CHD was higher than that among the HCs [15 (75%) vs 6 (30%), P = 0.004]. Three (15%) patients with CHD and 2 (10%) HCs had diabetes (P = 0.677). The 2 groups were compared using the t-test and the χ2 test. Other clinical characteristics and laboratory indexes are listed in Table 1. Table 1: Characteristics of patients with coronary heart disease and healthy controls in the exploration stage Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or the χ2 test. P-value <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HCs: healthy controls; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Table 1: Characteristics of patients with coronary heart disease and healthy controls in the exploration stage Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      Parameters  Patients with CHD (n = 20)  HCs (n = 20)  P-value  Age (years)  59.6 ± 9.7  57.2 ± 8.5  0.410  Male  4 (20)  5 (25)  0.705  BMI (kg/m2)  24.0 ± 2.8  23.2 ± 2.9  0.380  Hypertension  15 (75)  6 (30)  0.004  Diabetes  3 (15)  2 (10)  0.677  Smoker  11 (55)  6 (30)  0.110  Family history of CHD  7 (35)  3 (15)  0.144  CAOD extent (%)  85.00 (80.00–90.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  30.00 (12.50–60.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or the χ2 test. P-value <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HCs: healthy controls; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Differentially expressed micro-ribonucleic acids in the exploration stage In the exploration stage, the expression of 14 pro-angiogenic miRNAs in plasma was determined in 20 patients with CHD and in 20 HCs using the quantitative real-time polymerase chain reaction method. As shown in Table 2, the expression of miR-126 (P = 0.043), miR-17-5p (P = 0.019), miR-19a (P = 0.043), miR-92a (P = 0.047), miR-210 (P = 0.048) and miR-378 (P = 0.033) in patients with CHD was down-regulated compared with that in the HCs. The comparison between the 2 groups were compared using the Mann–Whitney test. The mean differences in the expressions of miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 in patients with CHD and HCs were 3.11, 3.76, 3.29, 3.86, 3.12 and 2.84, respectively, which indicated that the differences were biologically significant. These 6 differentially expressed miRNAs were subsequently selected to be demonstrated in the validation stage. The full sequences of the 14 miRNAs are listed in Supplementary Material, Table S1. Table 2: Fourteen circulating pro-angiogenic miRNAs in patients with coronary heart disease and in healthy controls in the exploration stage Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  The boldface values stand for values with statistical significance. Data were presented as mean, SD, median and 95% CI. Comparison was determined by the Mann–Whitney test. P-value <0.05 was considered significant. CHD: coronary heart disease; CI: confidence interval; HC: healthy control; miRNA: micro-ribonucleic acid; SD: standard deviation. Table 2: Fourteen circulating pro-angiogenic miRNAs in patients with coronary heart disease and in healthy controls in the exploration stage Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  Items  Patients with CHD (n = 20)   HCs (n = 20)   Mean difference  P-value  Mean  SD  Median  95% CI  Mean  SD  Median  95% CI  miR-126  6.47  4.23  6.63  4.49–8.45  9.58  5.14  8.74  7.14–11.99  3.11  0.043  miR-17-5p  5.41  3.82  4.95  3.62–7.20  9.17  5.68  7.86  6.51–11.83  3.76  0.019  miR-17-3p  4.37  3.18  4.21  2.88–5.86  6.09  3.84  5.52  4.29–7.89  1.72  0.131  miR-18a  5.25  3.26  5.47  3.72–6.78  6.07  3.63  5.75  4.37–7.77  0.82  0.457  miR-19a  5.54  3.81  5.21  3.76–7.32  8.83  5.87  7.97  6.08–11.58  3.29  0.043  miR-20a  7.49  5.27  6.87  5.02–9.96  9.05  6.34  8.76  6.08–12.02  1.56  0.403  miR-19b-1  6.58  3.26  6.43  5.05–8.11  7.41  3.86  7.02  5.60–9.22  0.83  0.467  miR-92a  7.42  5.03  7.11  5.07–9.77  11.28  6.73  11.81  8.13–14.43  3.86  0.047  let-7b  5.71  2.58  5.46  4.50–6.92  7.14  4.63  7.44  4.97–9.31  1.43  0.237  let-7f  6.16  4.27  6.31  4.16–8.16  7.83  4.90  8.03  5.54–10.12  1.67  0.258  miR-130a  7.40  4.69  7.21  5.21–9.59  9.25  6.83  8.93  6.05–12.45  1.85  0.325  miR-210  5.61  3.84  5.22  3.81–7.41  8.73  5.62  8.74  6.10–11.36  3.12  0.048  miR-378  6.29  4.11  6.10  4.37–8.21  9.53  5.12  9.59  7.13–11.93  3.24  0.033  miR-296  7.04  4.26  6.93  5.05–9.03  9.88  6.10  8.83  7.03–12.73  2.84  0.096  The boldface values stand for values with statistical significance. Data were presented as mean, SD, median and 95% CI. Comparison was determined by the Mann–Whitney test. P-value <0.05 was considered significant. CHD: coronary heart disease; CI: confidence interval; HC: healthy control; miRNA: micro-ribonucleic acid; SD: standard deviation. Characteristics of patients with coronary heart disease and the healthy controls in the validation stage As shown in Table 3, the mean ages were 60.2 ± 11.4 years and 57.9 ± 14.8 years in patients with CHD and the HCs (P = 0.231), respectively. There were 81 women and 21 men in the patients with CHD group and 66 women and 26 men in the HC group (P = 0.213). The mean body mass index of patients with CHD and the HCs was 24.2 ± 3.7 kg/m2 and 23.6 ± 3.5 kg/m2, respectively (P = 0.249). The number of participants who had hypertension [73 (72%) vs 38 (41%), P < 0.001], diabetes [22 (22%) vs 10 (11%), P = 0.045] and a history of smoking [49 (48%) vs 31 (34%), P = 0.043] in patients with CHD was greater than that in the HCs. The 2 groups were compared using the t-test and the χ2 test. Other information about clinical characteristics, disease history and laboratory indexes is listed in Table 3. Table 3: Characteristics of patients with coronary heart disease and healthy controls in the validation stage Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or χ2 test. P-value  <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HC: healthy control; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Table 3: Characteristics of patients with coronary heart disease and healthy controls in the validation stage Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      Parameters  Patients with CHD (n = 102)  HCs (n = 92)  P-value  Age (years)  60.2 ± 11.4  57.9 ± 14.8  0.231  Male  21 (21)  26 (28)  0.213  BMI (kg/m2)  24.2 ± 3.7  23.6 ± 3.5  0.249  Hypertension  73 (72)  38 (41)  <0.001  Diabetes  22 (22)  10 (11)  0.045  Smoke  49 (48)  31 (34)  0.043  Family history of CHD  36 (35)  23 (25)  0.120  CAOD extent (%)  85.00 (75.00–95.00)      TG (mmol/l)  1.85 ± 0.86      TC (mmol/l)  4.39 ± 1.53      HDL-C (mmol/l)  1.08 ± 0.35      LDL-C (mmol/l)  2.94 ± 1.10      Gensini score  44.50 (20.00–68.00)      The boldface values stand for values with statistical significance. Data were presented as mean ± standard deviation, median (25th–75th percentile value) or n (%). The 2 groups were compared using the t-test or χ2 test. P-value  <0.05 was considered significant. BMI: body mass index; CAOD: coronary arterial occlusive disease; CHD: coronary heart disease; HC: healthy control; HDL-C: fasting high-density lipoprotein cholesterol; LDL-C: fasting low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. Expression of selected micro-ribonucleic acids in the validation stage In the validation stage, 6 selected differentially expressed miRNAs were further measured by the quantitative real-time polymerase chain reaction in a large population of 102 patients with CHD and 92 HCs. The levels of miR-126 (P = 0.001), miR-17-5p (P = 0.008), miR-92a (P = 0.003), miR-210 (P = 0.002) and miR-378 (P = 0.040) were remarkably decreased in patients with CHD compared with HCs (Fig. 2). However, no difference in the miR-19a plasma level was observed between patients with CHD and HCs (P = 0.091). The 2 groups were compared using the t-test. Figure 2: View largeDownload slide Plasma levels of 6 differentially expressed micro-ribonucleic acids from patients with CHD and HCs in the validation stage. (A) Plasma level of miR-126 in patients with CHD and in HCs. (B) Plasma level of miR-17-5p in patients with CHD and in HCs. (C) Plasma level of miR-19a in patients with CHD and in HCs. (D) Plasma level of miR-92a in patients with CHD and in HCs. (E) Plasma level of miR-210 in patients with CHD and in HCs. (F) Plasma level of miR-378 in patients with CHD and in HCs. The 2 groups were compared using the t-test. P-value <0.05 was considered significant. CHD: coronary heart disease; HC: healthy control. Figure 2: View largeDownload slide Plasma levels of 6 differentially expressed micro-ribonucleic acids from patients with CHD and HCs in the validation stage. (A) Plasma level of miR-126 in patients with CHD and in HCs. (B) Plasma level of miR-17-5p in patients with CHD and in HCs. (C) Plasma level of miR-19a in patients with CHD and in HCs. (D) Plasma level of miR-92a in patients with CHD and in HCs. (E) Plasma level of miR-210 in patients with CHD and in HCs. (F) Plasma level of miR-378 in patients with CHD and in HCs. The 2 groups were compared using the t-test. P-value <0.05 was considered significant. CHD: coronary heart disease; HC: healthy control. To evaluate the levels of the 6 differentially expressed miRNAs in the exploration stage for predicting the risk of CHD, univariable and multivariable logistic regression analyses were performed in the validation stage. As listed in Table 4, plasma levels of miR-126 (P = 0.001), miR-17-5p (P = 0.008), miR-92a (P = 0.003), miR-210 (P = 0.002) and miR-378 (P = 0.039) were protective factors for CHD, whereas expression of miR-19a (P = 0.085) in plasma was not a predictive factor for CHD. Multivariate regression analysis was performed using a stepwise method, which confirmed that plasma levels of miR-126 (P = 0.001), miR-17-5p (P = 0.013), miR-92a (P = 0.016), miR-210 (P < 0.001) and miR-378 (P = 0.019) were independent predictive factors for CHD. Table 4: Logistic regression analysis for 6 differentially expressed micro-ribonucleic acids in the validation stage   Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981    Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981  The boldface values stand for values with statistical significance. Data were presented as P-value, OR and 95% CI. Significance was determined by univariable and multivariable logistic regression analysis. P-value <0.05 was considered significant. CI: confidence interval; OR: odds ratio. Table 4: Logistic regression analysis for 6 differentially expressed micro-ribonucleic acids in the validation stage   Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981    Univariable logistic regression (n = 194)   Multivariable logistic regression (n = 194)   P-value  OR  95% CI   P-value  OR  95% CI       Lower  Higher      Lower  Higher  miR-126  0.001  0.878  0.813  0.948  0.001  0.863  0.791  0.941  miR-17-5p  0.008  0.875  0.793  0.966  0.013  0.867  0.775  0.971  miR-19a  0.085  0.928  0.853  1.010          miR-92a  0.003  0.886  0.817  0.960  0.016  0.896  0.82  0.98  miR-210  0.002  0.873  0.801  0.951  <0.001  0.839  0.761  0.924  miR-378  0.039  0.914  0.840  0.995  0.019  0.889  0.805  0.981  The boldface values stand for values with statistical significance. Data were presented as P-value, OR and 95% CI. Significance was determined by univariable and multivariable logistic regression analysis. P-value <0.05 was considered significant. CI: confidence interval; OR: odds ratio. Diagnostic value of selected micro-ribonucleic acids for coronary heart disease To evaluate the independent predictive value of miRNAs for the diagnosis of CHD, we conducted an ROC curve analysis. The area under the curve (AUC) of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 for predicting CHD was 0.641 [95% confidence interval (CI) 0.564–0.719], 0.609 (95% CI 0.526–0.692), 0.622 (95% CI 0.541–0.702), 0.617 (95% CI 0.537–0.698) and 0.574 (95% CI 0.492–0.656), respectively. When we combined the 5 selected miRNAs, the AUC was 0.756 (95% CI 0.687–0.725) with a sensitivity of 84.3% and a specificity of 60.9% at the best cut-off point, which indicated a good diagnostic value for CHD (Fig. 3). Figure 3: View largeDownload slide Receiver operating characteristic curves of 5 differentially expressed micro-ribonucleic acids in the validation stage for coronary artery disease. The analysis was determined by receiver operating characteristic curve analysis. AUC: area under the curve; CI: confidence interval. Figure 3: View largeDownload slide Receiver operating characteristic curves of 5 differentially expressed micro-ribonucleic acids in the validation stage for coronary artery disease. The analysis was determined by receiver operating characteristic curve analysis. AUC: area under the curve; CI: confidence interval. Correlation of selected micro-ribonucleic acids with Gensini scores To detect the association of selected miRNAs with CHD severity, we analysed the association of plasma levels of selected miRNAs with Gensini scores. The plasma levels of miR-126 (r = −0.343, P < 0.001), miR-210 (r = −0.384, P < 0.001) and miR-378 (r = −0.224, P = 0.024) were negatively correlated with Gensini scores. However, no association of miR-17-5p (r = −0.087, P = 0.386), miR-19a (r = −0.082, P = 0.414) and miR-92a (r = 0.066, P = 0.510) with Gensini scores was found (Fig. 4). The correlations were also analysed using the Pearson test. Figure 4: View largeDownload slide Correlations of 6 differentially expressed micro-ribonucleic acids plasma levels compared with Gensini scores. (A) Correlation of miR-126 plasma level with Gensini scores. (B) Correlation of miR-17-5p plasma level with Gensini scores. (C) Correlation of miR-19a plasma level with Gensini scores. (D) Correlation of miR-92a plasma level with Gensini scores. (E) Correlation of miR-210 plasma level with Gensini scores. (F) Correlation of miR-378 plasma level with Gensini scores. The Pearson test was used to assess the correlations of Gensini scores with 6 differentially expressed micro-ribonucleic acids levels. P-value <0.05 was considered significant. Figure 4: View largeDownload slide Correlations of 6 differentially expressed micro-ribonucleic acids plasma levels compared with Gensini scores. (A) Correlation of miR-126 plasma level with Gensini scores. (B) Correlation of miR-17-5p plasma level with Gensini scores. (C) Correlation of miR-19a plasma level with Gensini scores. (D) Correlation of miR-92a plasma level with Gensini scores. (E) Correlation of miR-210 plasma level with Gensini scores. (F) Correlation of miR-378 plasma level with Gensini scores. The Pearson test was used to assess the correlations of Gensini scores with 6 differentially expressed micro-ribonucleic acids levels. P-value <0.05 was considered significant. Analysis in the validation stage including patients who were not included in the exploration stage As presented in Supplementary Material, Fig. S1, in the validation stage including patients who were not included in the exploration stage, the expressions of miR-126 (P < 0.001), miR-17-5p (P = 0.042), miR-92a (P = 0.017), miR-210 (P = 0.017) and miR-378 (P < 0.001) were down-regulated in patients with CHD compared with those in the HCs. However, the expression of miR-19a was similar in patients with CHD and HCs (P = 0.150). The 2 groups were compared using the t-test. In addition, univariable and multivariable logistic regression analyses were performed to evaluate the predictive value of 6 differentially expressed miRNAs for CHD. In univariable logistic regression analysis, miR-126 (P = 0.001), miR-17-5p (P = 0.039), miR-210 (P = 0.017) and miR-378 (P < 0.001) were protective factors for CHD (Supplementary Material, Table S2), whereas miR-19a (P = 0.137) and miR-92a (P = 0.075) were not correlated with the risk of CHD. Subsequently, the multivariable logistic regression analysis using a stepwise model indicated that miR-126 (P = 0.001), miR-210 (P = 0.002) and miR-378 (P < 0.001) were independently associated with CHD. The ROC curve analysis conducted in the validation stage only included patients who were not included in the exploration stage, which indicated that the AUC of miR-126, miR-210 and miR-378 was 0.666 (95% CI 0.581–0.751), 0.596 (95% CI 0.504–0.688) and 0.706 (95% CI 0.626–0.787), respectively. When we combined the 3 miRNAs, we found that the AUC was 0.792 (95% CI 0.722–0.862) with a sensitivity of 73.2% and a specificity of 73.6% at the best cut-off point (Supplementary Material, Fig. S2). The Pearson test was used to correlate the 6 differentially expressed miRNAs with Gensini scores. As presented in Supplementary Material, Fig. S3, the expressions of miR-126 (P < 0.001), miR-17-5p (P = 0.042), miR-19a (P = 0.150), miR-92a (P = 0.017), miR-210 (P = 0.027) and miR-378 (P = 0.895) were not correlated with Gensini score. DISCUSSION Patients with CHD have a high risk for heart attacks and strokes, which are predominant causes of CHD deaths [10]. Thus, a rapid, accurate diagnostic procedure to ensure early, efficient interventions is badly needed. miRNAs were reported to be associated with several CVDs, such as acute myocardial infarctions [11]. We evaluated 14 selected miRNAs to explore their potential roles in predicting the risk and severity of CHD. We found that (i) in the exploration stage, miR-126, miR-17-5p, miR-19a, miR-92a, miR-210 and miR-378 remarkably declined in patients with CHD compared with HCs. In the validation stage, only miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 levels were decreased in patients with CHD. Univariable and multivariable logistic regression showed that those miRNAs that were differentially expressed in the validation stage were independent protective factors for CHD. (ii) ROC curves indicated that the combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD; (iii) expressions of miR-126, miR-210 and miR-378 in plasma were negatively correlated with Gensini scores. Numerous studies have shown the angiogenic-related functions of miRNAs, some of which were pro-angiogenic and some of which were anti-angiogenic [12, 13]. MiR-126, located on chromosome 9q34.3, was shown to be richly expressed in endothelial cells [14]. In a study conducted on zebrafish, miR-126 was shown to mediate the endothelial cells by regulating the vascular endothelial growth factor responses, and the knockdown of miR-126 resulted in a loss of vascular integrity and haemorrhage in embryonic development [15]. Wang et al. [16] also discovered that miR-126 managed vascular integrity and angiogenesis in mutant animals by mediating growth factors such as vascular endothelial growth factor and fibroblast growth factor. miR-17-5p, another angiogenesis-related miRNA, was also reported to modulate angiogenesis by mediating the level of the antiangiogenic factor tissue inhibitor of metalloproteinase 1 in mice [17]. miR-92a, the angiogenesis of which is controversial, could suppress the angiogenic capabilities of mesenchymal stromal cells by downregulating the hepatocyte growth factor level [18]; and pretreatment with miR-92a strengthens capillary tube formation of human umbilical vein endothelial cells under oxidative stress [19]. Although the role of miR-210 is relatively clear, Yang et al. [20] identified miR-210 as a pro-angiogenesis miRNA due to its function of enhancing cancer angiogenesis through targeting fibroblast growth factor receptor-like 1 in patients with hepatocellular carcinoma. Moreover, in the study of Bakirtzi et al. [21], colitis and intestinal angiogenesis were promoted by neurotensin through Hif-1α-miR-210 signalling. As reported in a previous study, miR-378 enhances cell migration, invasion and tumour angiogenesis in brain metastases of non-small-cell lung cancer [22]. Similarly, miR-378 was found to play a pro-angiogenic role in tumours by targeting SuFu and Fus-1 levels [23]. In our study, miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 plasma levels decreased in patients with CHD and were factors for predicting lower risk of CHD, which might result from their angiogenic function validated by prior studies, whereas no difference was found in plasma levels of miR-19a in patients with CHD and in HCs. The severity of CHD can be evaluated using single-photon emission computed tomography with a semiconductor gamma camera using thallium-201, which assesses the level of ischaemia and the Gensini score [24]. To evaluate the severity of CHD, we used the Gensini score, which is a scoring system widely used in clinical practice that includes the percentage of stenosis and the location of the coronary artery stenosis. Also, miR-126, miR-210 and miR-378 were negatively correlated with Gensini scores, which might result from the fact that these miRNAs were shown to participate in angiogenesis among multiple diseases including CHD [14–23]. Besides, the correlations of miR-126, miR-210 and miR-378 with Gensini score were relatively weak, which might result from the fact that some of the associations might not be linear, which may result in the relatively low correlation coefficients. In addition, previous researchers who investigated the correlation of plasma miRNA expressions with the Gensini score also reported relatively low correlation coefficients. For instance, Ding et al. [6] reported that the correlation coefficient of plasma miR-125b expression with the Gensini score of patients with CHD was −0.215 with a P-value of 0.017. Other studies showed that miRNAs are dysregulated and play crucial roles in the pathogenesis of non-coronary ischaemic tissue in other diseases and that ischaemia is one of the most critical clinical features of patients with CHD. Bijkerk et al. [25] showed that miR-126 could ameliorate ischaemia/reperfusion injury by enhancing vascular integrity. Platelet-derived miR-92a decreases the levels of cysteine protease inhibitor cystatin C in patients with Type II diabetes with lower limb ischaemia [26]. Additionally, miR-17-5p induced by p53 protects against ischaemia–reperfusion injury in the kidney by targeting the death receptor 6 [27]. A number of studies reported the diagnostic value of miRNAs in CVD or cardiovascular-related diseases. They showed that circulating miR-92a-3p, miR-126, miR-143, miR-145 and miR-223 may be involved in the pathogenesis of CVD and could serve as potential biomarkers for diagnosis and prognosis [28–30]. We first discovered that the combination of miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 was of good diagnostic value for CHD. The result might provide an option for the early diagnosis for CHD and improve the prognosis of patients with CHD. However, the exact mechanisms of these miRNAs in CHD are still unclear and were not investigated in our study. The sample size in this study was relatively small; thus, a study with a large sample size that measures the detailed mechanisms should be conducted in the future. CONCLUSION In conclusion, our study showed that circulating miR-126, miR-17-5p, miR-92a, miR-210 and miR-378 could serve as novel, promising biomarkers for the risk and severity of CHD. In addition, miR-126, miR-210 and miR-378 were negatively associated with Gensini scores. SUPPLEMENTARY MATERIAL Supplementary material is available at ICVTS online. Conflict of interest: none declared. REFERENCES 1 Sayed AS, Xia K, Salma U, Yang T, Peng J. Diagnosis, prognosis and therapeutic role of circulating miRNAs in cardiovascular diseases. Heart Lung Circ  2014; 23: 503– 10. Google Scholar CrossRef Search ADS PubMed  2 Greenwood JP, Maredia N, Younger JF, Brown JM, Nixon J, Everett CC et al.   Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet  2012; 379: 453– 60. Google Scholar CrossRef Search ADS PubMed  3 SCOT-HEART investigators. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet  2015; 385: 2383– 91. CrossRef Search ADS PubMed  4 Plasterk RH. Micro RNAs in animal development. Cell  2006; 124: 877– 81. Google Scholar CrossRef Search ADS PubMed  5 Urbich C, Kuehbacher A, Dimmeler S. Role of microRNAs in vascular diseases, inflammation, and angiogenesis. Cardiovasc Res  2008; 79: 581– 8. Google Scholar CrossRef Search ADS PubMed  6 Ding XQ, Ge PC, Liu Z, Jia H, Chen X, An FH et al.   Interaction between microRNA expression and classical risk factors in the risk of coronary heart disease. Sci Rep  2015; 5: 14925. Google Scholar CrossRef Search ADS PubMed  7 Ferrara N, Alitalo K. Clinical applications of angiogenic growth factors and their inhibitors. Nat Med  1999; 5: 1359– 64. Google Scholar CrossRef Search ADS PubMed  8 Yla-Herttuala S, Rissanen TT, Vajanto I, Hartikainen J. Vascular endothelial growth factors: biology and current status of clinical applications in cardiovascular medicine. J Am Coll Cardiol  2007; 49: 1015– 26. Google Scholar CrossRef Search ADS PubMed  9 Gensini GG. A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol  1983; 51: 606. Google Scholar CrossRef Search ADS PubMed  10 Who Cardiovascular Disease. http://www.Who.Int/topics/cardiovascular_diseases/en/ (14 March 2011, date last accessed). 11 Peng L, Chun-Guang Q, Bei-Fang L, Xue-Zhi D, Zi-Hao W, Yun-Fu L et al.   Clinical impact of circulating miR-133, miR-1291 and miR-663b in plasma of patients with acute myocardial infarction. Diagn Pathol  2014; 9: 89. Google Scholar CrossRef Search ADS PubMed  12 Wu F, Yang Z, Li G. Role of specific microRNAs for endothelial function and angiogenesis. Biochem Biophys Res Commun  2009; 386: 549– 53. Google Scholar CrossRef Search ADS PubMed  13 Jin F, Xing J. Circulating pro-angiogenic and anti-angiogenic microRNA expressions in patients with acute ischemic stroke and their association with disease severity. Neurol Sci  2017; 38: 2015– 23. Google Scholar CrossRef Search ADS PubMed  14 Parker LH, Schmidt M, Jin SW, Gray AM, Beis D, Pham T et al.   The endothelial-cell-derived secreted factor Egfl7 regulates vascular tube formation. Nature  2004; 428: 754– 8. Google Scholar CrossRef Search ADS PubMed  15 Fish JE, Santoro MM, Morton SU, Yu S, Yeh RF, Wythe JD et al.   miR-126 regulates angiogenic signaling and vascular integrity. Dev Cell  2008; 15: 272– 84. Google Scholar CrossRef Search ADS PubMed  16 Wang S, Aurora AB, Johnson BA, Qi X, McAnally J, Hill JA et al.   The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis. Dev Cell  2008; 15: 261– 71. Google Scholar CrossRef Search ADS PubMed  17 Otsuka M, Zheng M, Hayashi M, Lee JD, Yoshino O, Lin S et al.   Impaired microRNA processing causes corpus luteum insufficiency and infertility in mice. J Clin Invest  2008; 118: 1944– 54. Google Scholar CrossRef Search ADS PubMed  18 Kalinina N, Klink G, Glukhanyuk E, Lopatina T, Efimenko A, Akopyan Z et al.   miR-92a regulates angiogenic activity of adipose-derived mesenchymal stromal cells. Exp Cell Res  2015; 339: 61– 6. Google Scholar CrossRef Search ADS PubMed  19 Zhang L, Zhou M, Qin G, Weintraub NL, Tang Y. MiR-92a regulates viability and angiogenesis of endothelial cells under oxidative stress. Biochem Biophys Res Commun  2014; 446: 952– 8. Google Scholar CrossRef Search ADS PubMed  20 Yang Y, Zhang J, Xia T, Li G, Tian T, Wang M et al.   MicroRNA-210 promotes cancer angiogenesis by targeting fibroblast growth factor receptor-like 1 in hepatocellular carcinoma. Oncol Rep  2016; 36: 2553– 62. Google Scholar CrossRef Search ADS PubMed  21 Bakirtzi K, Law IK, Xue X, Iliopoulos D, Shah YM, Pothoulakis C. Neurotensin promotes the development of colitis and intestinal angiogenesis via Hif-1α-miR-210 signaling. J Immunol  2016; 196: 4311– 21. Google Scholar CrossRef Search ADS PubMed  22 Chen LT, Xu SD, Xu H, Zhang JF, Ning JF, Wang SF. MicroRNA-378 is associated with non-small cell lung cancer brain metastasis by promoting cell migration, invasion and tumor angiogenesis. Med Oncol  2012; 29: 1673– 80. Google Scholar CrossRef Search ADS PubMed  23 Lee DY, Deng Z, Wang CH, Yang BB. MicroRNA-378 promotes cell survival, tumor growth, and angiogenesis by targeting SuFu and Fus-1 expression. Proc Natl Acad Sci USA  2007; 104: 20350– 5. Google Scholar CrossRef Search ADS PubMed  24 Shiraishi S, Sakamoto F, Tsuda N, Yoshida M, Tomiguchi S, Utsunomiya D et al.   Prediction of left main or 3-vessel disease using myocardial perfusion reserve on dynamic thallium-201 single-photon emission computed tomography with a semiconductor gamma camera. Circ J  2015; 79: 623– 31. Google Scholar CrossRef Search ADS PubMed  25 Bijkerk R, van Solingen C, de Boer HC, van der Pol P, Khairoun M, de Bruin RG et al.   Hematopoietic microRNA-126 protects against renal ischemia/reperfusion injury by promoting vascular integrity. J Am Soc Nephrol  2014; 25: 1710– 22. Google Scholar CrossRef Search ADS PubMed  26 Zhang Y, Guan Q, Jin X. Platelet-derived miR-92a downregulates cysteine protease inhibitor cystatin C in type II diabetic lower limb ischemia. Exp Ther Med  2015; 9: 2257– 62. Google Scholar CrossRef Search ADS PubMed  27 Hao J, Wei Q, Mei S, Li L, Su Y, Mei C et al.   Induction of microRNA-17-5p by p53 protects against renal ischemia-reperfusion injury by targeting death receptor 6. Kidney Int  2017; 91: 106– 18. Google Scholar CrossRef Search ADS PubMed  28 Rong X, Jia L, Hong L, Pan L, Xue X, Zhang C et al.   Serum miR-92a-3p as a new potential biomarker for diagnosis of kawasaki disease with coronary artery lesions. J Cardiovasc Transl Res  2017; 10: 1– 8. Google Scholar CrossRef Search ADS PubMed  29 Massy ZA, Metzinger-Le Meuth V, Metzinger L. MicroRNAs are associated with uremic toxicity, cardiovascular calcification, and disease. Contrib Nephrol  2017; 189: 160– 8. Google Scholar CrossRef Search ADS PubMed  30 Cavarretta E, Frati G. MicroRNAs in coronary heart disease: ready to enter the clinical arena? Biomed Res Int  2016; 2016: 2150763. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Interactive CardioVascular and Thoracic SurgeryOxford University Press

Published: Mar 28, 2018

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