miR-941 as a promising biomarker for acute coronary syndrome

Patients and study design

A total of 72 patients with chest pain who underwent diagnostic coronary angiography in Xiyuan Hospital, affiliated hospital of China Academy of Chinese Medical Sciences were enrolled from March 2015 to July 2015. The patients were divided into four groups as follows: 18 patients with STEMI, 18 patients NSTE-ACS, 20 patients with SA, and 16 patients without CAD. Patients in the control group did not have coronary stenosis as confirmed by coronary angiography, but had three or more risk factors for coronary heart disease (see schematic of the study in Fig. 1). Five cases were randomly selected from each group for analysis of gene expression profiles, using an Affymetrix GeneChip miRNA4.0. The differential expression of miRNAs between groups was analyzed according to the following criteria: fold-change (FC), ≥ 1.5 and P < 0.05. qRT-PCR was applied to verify the differential expression of miRNAs (FC, ≥ 2; P < 0.05). This study was registered in the Chinese Clinical Trial Registry (no. ChiCTR-IPR- 15006336). The study was performed according to the guidelines of the Declaration of Helsinki and was approved by the Xiyuan Hospital Ethics Committee.

Diagnostic criteria

In this study, patients with coronary heart disease were classified as having SA or ACS, including STEMI, NSTE-ACS, and UA. Diagnostic criteria was refered to the 2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease [16], 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes [2], and 2013 ACCF/AHA Guideline for the Management of ST-elevation Myocardial Infarction [17]. In addition, all patients presented stenosis (≥ 50% in at least one main coronary artery) as confirmed by coronary angiography [18].

Inclusion and exclusion criteria

The inclusion criteria were as follows: patients who met the diagnostic criteria, were 35–75 years old, and provided informed consent. The exclusion criteria were as follows: Patients with combined diseases, such as cardiomyopathy, valvular heart disease, severe arrhythmia, heart failure, and other accompanying diseases; patients encountered challenges with data collection, such as religious or language barriers; patients were pregnant or lactating and participating in other clinical studies.

Blood collection and storage

Venous blood samples were collected via antecubital venipuncture from each subject within 3–5 h of the onset of symptoms but before arteriography. Whole blood samples (2 mL) were collected directly into EDTA-containing tubes (BD, Franklin Lakes, NJ, USA), and three volumes of red blood cell lysis buffer (NH4CL2009; Haoyang, Tianjin, China) was added to obtain leukocytes, which were isolated within 2 h by centrifugation at 3000 rpm for 5 min at 4 °C to remove other blood elements. Next, 1 mL TRIzol (15596–026; Invitrogen Life Technologies) was added, and samples were then transferred to RNase/Dnase-free tubes and stored at −80 °C.

RNA isolation and preparation

After collection of all samples, total RNA in leukocytes was isolated using a miRVana RNA Isolation Kit (p/n AM1556; Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s specifications. Samples were then subjected to on-column DNase I treatment with RNase-free Dnase (#79254; Qiagen, Valencia, CA, USA). RNA quantity and quality were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) and an Agilent 2100 bioanalyzer. The RNA integrity was evaluated by agarose gel electrophoresis with ethidium bromide staining.

miRNA array analysis

Microarray analysis of gene expression was carried out using ELOSA QC Assays prior to array hybridization. Sample labeling, microarray hybridization, and washing were performed based on the manufacturer’s standard protocols. Briefly, total RNA was modified with poly A tails and then labeled with biotin. Next, the labeled RNAs were hybridized onto the microarray. Slides were washed and stained, and the arrays were scanned using an Affymetrix Scanner 3000 (Affymetrix).

miRNA microarray analysis was performed to determine differential expression of blood-borne miRNAs among (i) non-CAD individuals and patients with STEMI (n = 5), (ii) patients with SA and STEMI (n = 5), and (iii) patients with NSTE-ACS and STEMI. Bioinformatic determination of downstream predicted targets for candidate miRNAs was performed as described previously by Selbach et al. [19].

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) analysis

Quantification was performed through two-step reaction process: reverse transcription (RT) and qPCR. Each RT reaction consisted of 1 μg RNA, 4 μL miScript HiSpec Buffer, 2 μL Nucleics Mix, and 2 μL miScript Reverse Transcriptase Mix (Qiagen, Germany), in a total volume of 20 μl. Reactions were performed in a GeneAmp PCR System 9700 (Applied Biosystems, USA) for 60 min at 37 °C, followed by heat inactivation of the reverse transcriptase for 5 min at 95 °C. The 20-μL reaction mix was then diluted 5-fold in nuclease-free water and stored at −20 °C.

Real-time PCR was performed using a LightCycler 480II Real-time PCR Instrument (Roche, Switzerland) with 10 μL of the reaction mixture including 1 μL cDNA, 5 μL 2× LightCycler 480 SYBR Green I Master (Roche), 0.2 μL universal primers (Qiagen), 0.2 μL miRNA-specific primer, and 3.6 μL nuclease-free water. Reactions were incubated in a 384-well optical plate (Roche) at 95 °C for 10 min, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. Triplicates were averaged to calculate the expression value for each sample. At the end of the PCR cycling, melt curve analysis was performed to validate the specific generation of the expected PCR product. miRNA-specific primer sequences were designed in the laboratory and synthesized by Generay Biotech (Generay, PRC) based on the miRNA sequences obtained from the miRBase database (Release 20.0) as follows: hsa-miR-182-5p, UUUGGCAAUGGUAGAACUCACACU; hsa-miR-363-3p, AAUUGCACGGUAUCCAUCUGUA; and hsa-miR-941, CACCCGGCUGUGUGCACAUGUGC).

The expression levels of miRNAs were normalized to U6 and were calculated by the 2^-ΔΔCt method [20].

Statistical analysis

Affymetrix GeneChip Command Console software (version4.0, Affymetrix) was used to analyze array images to obtain raw data, and RMA normalization was then carried out. Next, Genespring software (version 12.5, Agilent Technologies) was used for subsequent data analysis. Differentially expressed miRNAs were then identified through fold changes, and P-values were calculated using t-tests. The threshold set for up- and downregulated genes was a fold change of 1.5 or more and a P value of less than 0.05. Target genes of differentially expressed miRNAs were the intersection predicted with three databases (Targetscan, PITA, and microRNAorg). Gene ontology (GO) analysis and KEGG analysis were applied to determine the roles of these target genes. Hierarchical clustering was performed to show distinguishable miRNA expression patterns among samples.

Paired and unpaired Student’s t tests were performed to compare data as appropriate. Values are expressed as means ± standard deviations (SDs). P values of less than 0.05 (two-sided) were considered significant.

Patient clinicopathological information

miRNA array analysis

Gene ontology (GO) analysis

Differentially expressed miRNAs in patients without CAD and with SA or NSTE-ACS (compared with that in patients with STEMI) were found to be involved in inflammation, protein phosphorylation, RNA polymerase II-dependent transcription, cell adhesion, and other biological processes. Among these, inflammation, cell adhesion, T-cell proliferation, calcium transfer, and apoptosis were closely related to atherosclerosis (Fig. 2).

Pathway analysis

The KEGG database was used to analyze target genes of differentially expressed miRNAs. Differentially expressed miRNAs in control and in patients with SA or NSTE-ACS compared with that in patients with STEMI were found to regulate biological processes such as mitogen-activated protein kinase (MAPK) signaling, tumorigenesis, and calcium ion signaling. Among the identified cell signaling pathways, we identified the following pathways involved in the pathological process of atherosclerosis: adhesion molecules, ErbB signaling, metabolism, Wnt signaling, insulin signaling, apoptosis, vascular endothelial growth factor signaling, and cell factor receptor interactions (Fig. 3).

miRNA-pathway network analysis showed that miR-941 can participate in regulation of T-cell receptor signaling, insulin signaling, and MAPK signaling. Additionally, miR-182-5p was related to vascular smooth muscle cell constriction and mammalian target of rapamycin (mTOR) signaling. miR-363-3p was involved in Toll-like receptor signaling and actin cytoskeleton regulation. Bioinformatics functional predictions showed that the differential expressed miRNAs could be related to the pathological process of cardiovascular disease (Fig. 4).

qRT-PCR

Next, we performed qRT-PCR to verify the different expression of candidated gene like, miR-941, miR-182-5p, and miR-363-3p.The results showed that miR-941 expression has no significant difference between the control patients (without CAD) and SA group. However, comparing with control patients, miR-941 expression was significantly increased by 1.64- and 2.28-fold in patients with NSTE-ACS and STEMI, respectively, (P < 0.05; Fig. 5a). There were no significant differences in miR-182-5p and miR-363-3p among groups (P > 0.05; Fig. 5b and c). Additionally, the expression of miR-941 was increased by 1.66-fold in patients with STEMI compared with that in patients with SA (P < 0.001; Fig. 6a-1) and by 1.52-fold in patients with STEMI compared with that in patients with NSTE-ACS (P < 0.05; Fig. 6a-2). There were no significant differences in miR-182-5p and miR-363-3p (P > 0.05; Fig. 6b and c).

Since there were no significant differences in miR-941 expression between patients without CAD and patients with SA, we combined the two groups into the non-ACS group. Compared with that in the non-ACS group, the expression of miR-941 was upregulated in the ACS group (FC: 1.62; P < 0.01; Fig. 7). NSTE-ACS and STEMI can reflect the degree of disease progression, and miR-941 was significantly upregulated in patients with STEMI compared with that in patients in the non-ACS group (FC: 1.52; P < 0.05; Fig. 6a-2). The differential expression of miR-941 in the two groups showed that the expression of miR-941 was associated with the severity of ACS.

Receiver operating characteristic (ROC) curve analysis

Next, we performed ROC curve analysis to test the reliability of miR-941 as a diagnostic biomarker of STEMI and ACS. Comparing with patients with STEMI, areas under the ROC curves were 0.896, 0.808, and 0.781 for patients in the control, SA, and NSTE-ACS groups, respectively; Comparing with patients in ACS group,the ROC curves of non-ACS group was 0.734 (Fig. 8). Thus, miR-941 may act as a reliable biomarker of ACS, particularly STEMI. The area under the curve (AUC), 95% confidence intervals (CIs), and p values are summarized in Table 4.

miR-941	AUC	95% CI	P value
Non-ACS versus ACS	0.734	0.619–0.848	0.001
Without CAD versus STEMI	0.896	0.779–1.000	0.000
SA versus STEMI	0.808	0.670–0.947	0.001
NSTE-ACS versus STEMI	0.781	0.622–0.939	0.004

Abbreviations: SA stable angina, ACS acute coronary syndrome, NSTE-ACS non-ST segment elevation acute coronary syndrome, STEMI ST segment elevation acute myocardial infarction, CAD coronary artery disease, ROC receiver operating characteristic, AUC area under the curve