Skip to main content
Download PDF

miR-941 as a promising biomarker for acute coronary syndrome

BMC Cardiovascular Disorders volume 17, Article number: 227 (2017) Cite this article

Abstract

Background

Circulating miRNAs can function as biomarkers for diagnosis, treatment, and prevention of diseases. However, it is unclear whether miRNAs can be used as biomarkers for acute coronary syndrome (ACS). To this end, we applied gene chip technology to analyze miRNA expression in patients with stable angina (SA), non-ST elevation ACS (NSTE-ACS), and ST-segment elevation myocardial infarction (STEMI).

Methods

We enrolled patients with chest pain who underwent diagnostic coronary angiography, including five patients each with SA, NSTE-ACS, or STEMI, and five controls without coronary artery disease (CAD) but with three or more risk factors. After microarray analysis, differential miRNA expression was confirmed by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR).

Results

Compared with those in patients with STEMI, differentially expressed microRNAs in controls and patients with SA or NSTE-ACS were involved in inflammation, protein phosphorylation, and cell adhesion. Pathway analysis showed that differentially expressed miRNAs were related to the mitogen-activated protein kinase signaling, calcium ion pathways, and cell adhesion pathways. Compared with their expression levels in patients with STEMI, miR-941, miR-363-3p, and miR-182-5p were significantly up-regulated (fold-change: 2.0 or more, P < 0.05) in controls and patients with SA or NSTE-ACS. Further, qRT-PCR showed that plasma miR-941 level was elevated in patients with NSTE-ACS or STEMI as compared with that in patients without CAD (fold-change: 1.65 and 2.28, respectively; P < 0.05). Additionally, miR-941 expression was significantly elevated in the STEMI group compared with that in the SA (P < 0.01) and NSTE-ACS groups (P < 0.05). Similarly, miR-941 expression was higher in patients with ACS (NSTE-ACS or STEMI) than in patients without ACS (without CAD or with SA; P < 0.01). There were no significant differences in miR-182-5p and miR-363-3p expression. The areas under the receiver operating characteristic curves were 0.896, 0.808, and 0.781 for patients in the control, SA, and NSTE-ACS groups, respectively, compared with that for patients with STEMI; that for the ACS group compared with the non-ACS group was 0.734.

Conclusion

miR-941 expression was relatively higher in patients with ACS and STEMI. Thus, miR-941 may be a potential biomarker of ACS or STEMI.

Peer Review reports

Background

Acute coronary syndrome (ACS) is a major cause of death and disability [1]. Rapid antiplatelet therapy and revascularization could prevent myocardial ischemia and reduce the incidence of cardiovascular events [2]. Thus, the early diagnosis of non-ST elevation ACS (NSTE-ACS) and ST-segment elevation myocardial infarction (STEMI) is essential for improved prognoses. Currently, the clinical diagnosis of ACS relies on assessment of symptoms, ischemia changes in electrocardiogram (ECG), and changes in troponin [2]. However, in elderly individuals and patients with diabetes, typical symptoms are not always observed. Moreover, changes in ECG can be easily influenced by left bundle branch blockage and chronic myocardial infarction. To a certain extent, unstable angina pectoris (UA), non-STEMI (NSTEMI), and STEMI reflect the pathological progress of ACS. Therefore, identification of novel biomarkers may facilitate the early diagnosis of ACS, particularly in patients with atypical symptoms of ACS.

MicroRNAs (miRNAs) are endogenous, small (22–24-nucleotide) noncoding RNA molecules that regulate the expression of mRNA by combining with the 3′- untranslated region (3′-UTR), subsequently triggering the degradation of mRNA or having negative effects on transcription [3]. miRNAs can regulate nearly 60% of coding genes to exert their biological functions [4, 5]. Recently, many studies have shown that miRNAs can regulate endothelial dysfunction, inflammation, cell autophagy, platelet activation, and aggregation [6,7,8,9]. Moreover, miRNA expression can affect the stability of atherosclerotic plaques [10]. miRNAs possess tissue-specific expression and can be secreted into blood or urine.miRNAs of circulartory system can be used as biomarkers of diagnosis, treatment, and prevention of diseases, such as coronary heart disease [11,12,13,14]. However, it is still unclear whether miRNAs can be used as biomarkers of ACS and further evaluate the severity of ACS [15].

Therefore, in this study, we applied gene chip technology to analyze the expression of miRNAs in patients with stable angina (SA), NSTE-ACS, and STEMI. We then performed quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) to determine whether miRNAs could be used as biomarker for the diagnosis of ACS.

Methods

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.

Fig. 1
figure 1

Schematic of the study design

Full size image

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.

Results

Patient clinicopathological information

Seventy-two patients (18 patients with STEMI, 18 patients with NSTE-ACS, 20 patients with SA, and 16 patients without CAD) were enrolled in this study. The clinicopathological characteristics of the patients are presented in Tables 1 and 2. There were no significant differences in clinical features among groups (P > 0.05).

Table 1 Baseline clinical characteristics of different patient groups used in microarray analysis
Full size table
Table 2 Baseline clinical characteristics of different patient groups used in qRT-PCR analysis
Full size table

miRNA array analysis

The differential expression of miRNAs among the patient groups was determined by gene chip analysis. The results showed that there exist 13 differentially expressed miRNAs in patients with STEMI comparing with those in patients without CAD and patients with SA and NSTE-ACS. After that, we chose miR-941, miR-363-3p, and miR-182-5p (FC,≥ 2.0; P < 0.05) for further analysis by qRT-PCR (Table 3).

Table 3 Differentially expressed miRNAs in patients with ACS versus patients without CAD and patients with SA or NSTE-ACS in microarray analysis
Full size table

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).

Fig. 2
figure 2

Gene ontology (GO) analysis

Full size image

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).

Fig. 3
figure 3

Pathway analysis

Full size image

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).

Fig. 4
figure 4

Microarray pathway network. The red rectangles are miRNAs, the purple circle are pathways, and the green lines represented the regulatory connections between miRNAs and pathways

Full size image

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).

Fig. 5
figure 5

Expression profiles of candidate miRNAs in patients with and without CAD. The three candidate miRNAs were evaluated with qRT-PCR using 72 samples, including 16 non-CAD, 20 SA, 18 NSTE-ACS, and 18 STEMI samples. miR-941 was significantly upregulated in the SA, NSTE-ACS, and STEMI groups compared with that in non-CAD patients (a). miR-182-5p (b) and miR-363-3p (c) were not significantly different in patients with CAD (SA, NSTE-ACS, and STEMI) compared with that in patients without CAD. Abbreviations: CAD: coronary artery disease, SA: stable angina, NSTE-ACS: non-ST elevation acute coronary syndromes, STEMI: ST elevation myocardial infarction. *P < 0.05, Δ P < 0.001

Full size image
Fig. 6
figure 6

Expression profiles of candidate miRNAs in patients with SA, NSTE-ACS, and STEMI. The three candidate miRNAs were evaluated by qRT-PCR using 56 samples (20 cases of SA, 18 cases of NSTE-ACS, and 18 cases of STEMI). (a) miR-941, (b) miR-182-5p, and (c) miR-363-3p are shown. Abbreviations: CAD, coronary artery disease; SA, stable angina; NSTE-ACS, non-ST elevation acute coronary syndrome; STEMI, ST elevation myocardial infarction. *P < 0.05, Δ P < 0.001

Full size image

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.

Fig. 7
figure 7

Expression profiles of miR-941 in patients with and without ACS. miR-941 expression was evaluated by qRT-PCR using 72 samples (36 patients without ACS and 36 patients with ACS). Abbreviations: ACS, acute coronary syndrome. Δ P < 0.01

Full size image

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.

Fig. 8
figure 8

ROC curve analysis. a Expression profiles of miR-941 in patients with STEMI and without CAD. b Expression profiles of miR-941 in patients with STEMI and SA. c Expression profiles of miR-941 in patients with STEMI and NSTE-ACS. d Expression profiles of miR-941 in patients with and without ACS

Full size image
Table 4 ROC curve analysis of miR-941 expression in patients with ACS and STEMI
Full size table

Discussion

Aberrant miRNA expression has been involved with a number of human diseases, including cardiovascular diseases [11, 21,22,23]. ACS has become a major public health problem owing to its high mortality and morbidity [24,25,26]. Thus, there is an urgent need to identify new diagnostic and therapeutic biomarkers of ACS; miRNAs may have such applications.

MiRNAs are involved in endothelial dysfunction, inflammation, apoptosis, angiogenesis, atherosclerosis, and other pathological processes involved in cardiovascular diseases [6, 27, 28], and miRNAs of circulartory system may be potential biomarkers of these diseases [7, 29]. miR-135a, miR-31, miR-378, and miR-147 are biological markers of stable coronary heart disease [30]; miR-1, miR-126, and miR-133a have potential value in the diagnosis of UA [31]; and miR-208b, miR-499, and miR-1 play a key role in the diagnosis, progression, and prognosis of acute myocardial infarction (AMI) [15, 32,33,34]. However, it is unclear whether miRNAs are differentially expressed with changes in the severity of coronary heart disease, including coronary stenosis and myocardial damage. Our findings suggested that miR-363-3p, miR-941, and miR-182-5p were differentially expressed among groups of patients with various types and degrees of coronary heart disease. Additionally, GO and KEGG pathway analysis showed that the differentially expressed miRNAs were involved in inflammatory responses, immune responses, MAPK signal, calcium pathway, ErbB signaling, and other cellular processes. In particular, MAPK signaling and immune responses are involved in the pathogenesis of atherosclerosis and affect the stability of plaques and the formation of blood clots. Furthermore, qRT-PCR analysis verified that miR-941 was differentially expressed in plasma from patients with CADs (SA, NSTE-ACS, and STEMI) compared with that in patients without CAD. Thus, these data suggested that miR-941 may have applications as a biomarker of CAD, with the ability to distinguish among ACS and non-ACS groups and to distinguish STEMI from SA and NSTE-ACS. Notably, miR-941 was gradually upregulated as the degree of coronary artery stenosis and myocardial injury increased (SA < NSTE-ACS < STEMI); thus, miR-941 was closely associated with the severity of ACS. Accordingly, we concluded that miR-941 was a biomarker of ACS, particularly for STEMI, and could predict the severity and progression of coronary heart disease.

Recent bioinformatics analyses have shown that miR-941 is involved in Wnt signaling, transforming growth factor (TGF)-β signaling, and insulin signaling [35, 36]. However, no research has been reported the role of miR-941 in atherosclerosis and coronary heart disease. In our study, we found that miR-941 may be associated with metabolism, inflammation, cell proliferation, and other biological processes through regulation of components involved in insulin signaling, MAPK signaling, T-cell receptor signaling, and other related pathways. However, we were not able to confirm the direct relationship between miR-941 and the pathogenesis of atherosclerosis. In vitro and in vivo experiments are needed to clarify the biological function of miR-941 in the pathophysiology of atherosclerosis and to determine whether miR-941 is involved in pathological processes of inflammation, immunity, metabolism, and platelet activation. Although we found that miR-941 was differentially expressed between ACS and non-ACS groups, additional studies are needed to establish a rapid, inexpensive method for miRNA analysis. Additionally, greater sample sizes are needed to further confirm the potential applications of this miRNA as a promising diagnostic tool for diagnosis of ACS.

Conclusions

MiR-941 was relatively higher in patients with ACS and STEMI, and could predict the severity and progression of coronary heart disease. Thus, miR-941 may be a potential biomarker of ACS or STEMI.

Abbreviations

3′-UTR:

3′-untranslated region

ACS:

Acute coronary syndrome

AMI:

Acute myocardial infarction

AUC:

Area under the curve

CI:

Confidence interval

ECG:

Electrocardiogram

FC:

Fold-change

GO:

Gene ontology

MAPK:

Mitogen-activated protein kinase

MiRNA:

MicroRNA

MTOR:

Mammalian target of rapamycin

NSTE-ACS:

Non-ST elevation ACS

qRT-PCR:

Quantitative real-time reverse transcription polymerase chain reaction

ROC:

Receiver operating characteristic

SA:

Stable angina

STEMI:

ST-segment elevation myocardial infarction

TGF:

Transforming growth factor

UA:

Unstable angina pectoris

References

  1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014;129:399–401.

    Article  PubMed  Google Scholar 

  2. Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, et al. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64:e139–228.

    Article  PubMed  Google Scholar 

  3. Diehl P, Fricke A, Sander L, Stamm J, Bassler N, Htun N, et al. Microparticles: major transport vehicles for distinct microRNAs in circulation. Cardiovasc Res. 2012;93:633–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Thomson DW, Bracken CP, Goodall GJ. Experimental strategies for microRNA target identification. Nucleic Acids Res. 2011;39:6845–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Noren Hooten N, Fitzpatrick M, Wood WH 3rd, De S, Ejiogu N, Zhang Y, et al. Age-related changes in microRNA levels in serum. Aging. 2013;5:725–40.

    Article  PubMed  Google Scholar 

  6. Peng Y, Song L, Zhao M, Harmelink C, Debenedittis P, Cui X, et al. Critical roles of miRNA-mediated regulation of TGFβ signalling during mouse cardiogenesis. Cardiovasc Res. 2014;103:258–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Small EM, Olson EN. Pervasive roles of microRNAs in cardiovascular biology. Nature. 2011;469:336–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schroen B, Heymans S. Small but smart—microRNAs in the centre of inflammatory processes during cardiovascular diseases, the metabolic syndrome, and ageing. Cardiovasc Res. 2012;93:605–13.

    Article  CAS  PubMed  Google Scholar 

  9. Edelstein LC, Bray PF. MicroRNAs in platelet production and activation. Blood. 2011;117:5289–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Menghini R, Stohr R, Federici M. MicroRNAs in vascular aging and atherosclerosis. Ageing Res Rev. 2014;17:68–78.

    Article  CAS  PubMed  Google Scholar 

  11. Small EM, Frost RJ, Olson EN. MicroRNAs add a new dimension to cardiovascular disease. Circulation. 2010;121:1022–32.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kataoka M, Wang DZ. Non-coding RNAs including miRNAs and lncRNAs in cardio-vascular biology and disease. Cell. 2014;3:883–98.

    Article  Google Scholar 

  13. Creemers EE, Tijsen AJ, Pinto YM. Circulating microRNAs: novel biomarkers and extracellular communicators in cardiovascular disease? Circ Res. 2012;110:483–95.

    Article  CAS  PubMed  Google Scholar 

  14. van Rooij E. The art of microRNA research. Circ Res. 2011;108:219–34.

    Article  PubMed  Google Scholar 

  15. Li C, Pei F, Zhu X, Duan DD, Zeng C. Circulating microRNAs as novel and sensitive biomarkers of acute myocardial infarction. Clin Biochem. 2012;45:727–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fihn SD, Blankenship JC, Alexander KP, Bittl JA, Byrne JG, Fletcher BJ, et al. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiol. 2014;64:1929–49.

    Article  PubMed  Google Scholar 

  17. American College of Emergency Physicians, Society for Cardiovascular Angiography and Interventions, O'Gara PT, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;61:e78–140.

    Google Scholar 

  18. Libby P, Theroux P. Pathophysiology of coronary artery disease. Circulation. 2005;111:3481–8.

    Article  PubMed  Google Scholar 

  19. Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008;455:58–63.

    Article  CAS  PubMed  Google Scholar 

  20. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delata Delta C(T)) method. Methods. 2001;25:402–8.

    Article  CAS  PubMed  Google Scholar 

  21. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. Identification of novel genes coding for small expressed RNAs. Science. 2001;294:853–8.

    Article  CAS  PubMed  Google Scholar 

  22. Gomes da Silva AM, Silbiger VN. miRNAs as biomarkers of atrial fibrillation. Biomarkers. 2014;19:631–6.

    Article  CAS  PubMed  Google Scholar 

  23. Zeller T, Keller T, Ojeda F, Reichlin T, Twerenbold R, Tzikas S, et al. Assessment of microRNAs in patients with unstable angina pectoris. Eur Heart J. 2014;35:2106–14.

    Article  CAS  PubMed  Google Scholar 

  24. McManus DD, Lin H, Tanriverdi K, Quercio M, Yin X, Larson MG, et al. Relations between circulating microRNAs and atrial fibrillation: data from the Framingham Offspring Study. Heart Rhythm. 2014;11:663–9.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pagidipati NJ, Gaziano TA. Estimating deaths from cardiovascular disease: a review of global methodologies of mortality measurement. Circulation. 2013;127:749–56.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Arbab-Zadeh A, Nakano M, Virmani R, Fuster V. Acute coronary events. Circulation. 2012;125:1147–56.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dangwal S, Bang C, Thum T. Novel techniques and targets in cardiovascular microRNA research. Cardiovasc Res. 2012;93:545–54.

    Article  CAS  PubMed  Google Scholar 

  28. Fichtlscherer S, Zeiher AM, Dimmeler S. Circulating microRNAs: biomarkers or mediators of cardiovascular diseases? Arterioscler Thromb Vasc Biol. 2011;31:2383–90.

    Article  CAS  PubMed  Google Scholar 

  29. 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.

    Article  PubMed  Google Scholar 

  30. D'Alessandra Y, Carena MC, Spazzafumo L, Martinelli F, Bassetti B, Devanna P, et al. Diagnostic potential of plasmatic MicroRNA signatures in stable and unstable angina. PLoS One. 2013;8:e80345.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Long G, Wang F, Duan Q, Chen F, Yang S, Gong W, et al. Human circulating microRNA-1 and microRNA-126 as potential novel indicators for acute myocardial infarction. Int J Biol Sci. 2012;8:811–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sayed AS, Xia K, Yang TL, Peng J. Circulating microRNAs: a potential role in diagnosis and prognosis of acute myocardial infarction. Dis Markers. 2013;35:561–6.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Liu P, Qiu C, Li B, Ding X, Wang Z, Li Y, 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.

    Article  Google Scholar 

  34. Liebetrau C, Mollmann H, Dorr O, Szardien S, Troidl C, Willmer M, et al. Release kinetics of circulating muscle-enriched microRNAs in patients undergoing transcoronary ablation of septal hypertrophy. J Am Coll Cardiol. 2013;62:992–8.

    Article  CAS  PubMed  Google Scholar 

  35. Zhang PP, Wang XL, Zhao W, Qi B, Yang Q, Wan H, et al. DNA methylation-mediated repression of miR-941 enhances lysine (K)-specific demethylase 6B expression in hepatoma cells. J Biol Chem. 2014;289:24724–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hu HY, He L, Fominykh K, Yan Z, Guo Sm Zhang X, et al. Evolution of the human-specific microRNA miR-941. Nat Commun. 2012;3:1145.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Shanghai OE Biotech. Co., Ltd. for technical assistance. We thank all of the patients for agreeing to participate in our study.

Funding

Supported by the Traditional Chinese Medicine Public Welfare Scientific Research Project, State Administration of Traditional Chinese Medicine of People’s Republic of China (No. 201007001); and the National Natural Science Foundation of China (No. 81603489).

Availability of data and materials

All data from this study can be found in the Supplementary Material (Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15).

Author information

Authors and Affiliations

  1. Xiyuan Hospital, China Academy of Chinese Medical Sciences, 1 Xiyuan Caochang, Haidian district, Beijing, China

    Ruina Bai, Qiaoning Yang, Ruixi Xi, Lizhi Li, Dazhuo Shi & Keji Chen

Authors
  1. Ruina Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiaoning Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruixi Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lizhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dazhuo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keji Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RB and QY were responsible for this manuscript and data analysis; these authors contributed equally to this manuscript. RX was responsible for data collection and follow-up. LL and DS modified the paper and gave constructive suggestions. KC made substantial contributions to conception and design. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dazhuo Shi.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Xiyuan Hospital Ethics Committee (approval no 11: 2015XL-15-2), and all study participants provided written informed consent.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

The mass detection report of samples. (DOCX 136 kb)

Additional file 1:

Baseline Table 1:Original data of Baseline clinical characteristics of different groups used in microarray analysis. (XLSX 10 kb)

Additional file 3:

Baseline Table 2:Original data of Baseline clinical characteristics of different groups used in qRT-PCR analysis. (XLSX 11 kb)

Additional file 4:

MiRNA-gene-network and miRNA-targets-relation. (XLSX 382 kb)

Additional file 5:

Microarray pathway network: Pathway-miRNA-relation and microarray pathway network. The red rectangles are miRNAs, the purple circle are pathways, and the green lines represented the regulatory connections between miRNAs and pathways. (XLSX 137 kb)

Additional file 6:

Original data of Table 3: Differentially expressed miRNAs in microarray analysis. (XLSX 19 kb)

Additional file 7:

Original data of Fig. 5a: MiR-941 was significantly upregulated in the SA, NSTE-ACS, and STEMI groups compared with that in non-CAD patients. (XLSX 14 kb)

Additional file 8:

Original data of Fig. 5b: MiR-182-5p were not significantly different in patients with CAD (SA, NSTE-ACS, and STEMI) compared with that in patients without CAD. (XLSX 17 kb)

Additional file 9:

Original data of Fig. 5c: MiR-363-3p were not significantly different in patients with CAD (SA, NSTE-ACS, and STEMI) compared with that in patients without CAD. (XLSX 17 kb)

Additional file 10:

Original data of Fig. 6a-1: MiR-941 Expression profiles of candidate miRNAs in patients with SA, NSTE-ACS, and STEMI. The three candidate miRNAs were evaluated by qRT-PCR using 56 samples. (XLSX 13 kb)

Additional file 11:

Original data of Fig. 6a-2: MiR-941 Expression profiles of candidate miRNAs in patients with SA, NSTE-ACS, and STEMI. The three candidate miRNAs were evaluated by qRT-PCR using 56 samples. (XLSX 13 kb)

Additional file 12:

Original data of Fig. 6b: MiR-182-5p Expression profiles of candidate miRNAs in patients with SA, NSTE-ACS, and STEMI. The three candidate miRNAs were evaluated by qRT-PCR using 56 samples. (XLSX 15 kb)

Additional file 13:

Original data of Fig. 6c:MiR-363-3p Expression profiles of candidate miRNAs in patients with SA, NSTE-ACS, and STEMI. The three candidate miRNAs were evaluated by qRT-PCR using 56 samples. (XLSX 15 kb)

Additional file 14:

Original data of Fig. 7: Expression profiles of miR-941 in patients with and without ACS. (XLSX 15 kb)

Additional file 15:

Table 4-ROC curve: Original data of ROC curve analysis of miR-941 expression in patients with ACS and STEMI. (XLSX 12 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, R., Yang, Q., Xi, R. et al. miR-941 as a promising biomarker for acute coronary syndrome. BMC Cardiovasc Disord 17, 227 (2017). https://doi.org/10.1186/s12872-017-0653-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12872-017-0653-8

Keywords

  • microRNA
  • Stable angina
  • Acute ST-segment elevation myocardial infarction
  • Acute coronary artery disease

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us