Review

miRNAs as biomarkers of myocardial infarction: a step forward towards personalized medicine? Emeline Goretti1, Daniel R. Wagner1,2, and Yvan Devaux1 1

Laboratory of Cardiovascular Research, Centre de Recherche Public de la Sante´ (CRP-Sante´), 84 Val Fleuri, L-1526 Luxembourg, Luxembourg 2 Division of Cardiology, Centre Hospitalier, 4 rue Barble´, L-1210 Luxembourg, Luxembourg

miRNAs are small noncoding RNAs known to posttranscriptionally regulate gene expression. miRNAs are expressed in the heart where they regulate multiple pathophysiological processes. The discovery of stable cardiac miRNAs in the bloodstream has also motivated the investigation of their potential as biomarkers. This review gathers the current knowledge on the use of miRNAs as novel biomarkers to improve risk stratification, diagnosis, and prognosis of patients with myocardial infarction. In the rapidly evolving era of biomarkers, the potential of miRNAs as promising tools to move personalized medicine a step forward is discussed. miRNAs in cardiovascular diseases Cardiovascular disease (CVD) including stroke is the leading cause of death around the world (World Health Orgahttp://www.who.int/cardiovascular_diseases). nization; Among all CVDs, coronary artery disease (CAD) is the most common cause of death, and acute myocardial infarction (MI; see Glossary) is often the first manifestation. In cancer patients, personalized medicine has made giant leaps over the past decade, and in some cases biosignatures are used to guide diagnosis and treatment, and to inform about prognosis [1]. However, in the field of cardiac disease, personalized medicine is only in its infancy. Some risk scores have been developed to predict the risk of an individual to develop CVD, and biomarkers such as cardiac troponins (cTns) can aid in the diagnosis of MI [2]. However, high-sensitivity assays used to measure cTns lack specificity since elevated cTns can be found in patients without MI [3]. The ability to predict outcome after MI is even a more difficult task. Currently available prognostic tools are blood levels of N-terminal pro-brain natriuretic peptide (Nt-pro-BNP), echocardiography, and magnetic resonance imaging. These tools are suboptimal, therefore there is a need for novel tools and improved risk stratification, diagnosis, and prognosis of patients with MI.

Corresponding author: Devaux, Y. ([email protected]). Keywords: miRNAs; myocardial infarction; biomarker; therapeutic targets; personalized medicine. 1471-4914/ ß 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.molmed.2014.10.006

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Approximately 20 years ago, it was discovered that components of the genome that had been considered nonfunctional had gene regulatory capacity. Among these, miRNAs are small noncoding RNAs 18–22 nucleotides long that are able to post-transcriptionally regulate gene expression [4]. miRNAs bind the 30 untranslated region (UTR) of mRNA targets and inhibit their translation or induce their degradation. miRNAs can less frequently bind the 50 UTR instead of the 30 UTR of target mRNAs [5], and also may bind the coding region of a mRNA target [6]. So far, more than 2500 mature human miRNAs have been identified (http://www.mirbase.org). miRNAs regulate key functions in the healthy heart. While most studies have focused on the role of miRNAs in the diseased heart, a few recent reports describe their role in the normal function of the heart and in cardiac development [7,8]. In the cardiovascular system, miRNAs are ubiquitously expressed in different cell types such as cardiomyocytes and endothelial cells, and play key roles in cardiac development, apoptosis, Glossary Biomarker: a measurable indicator of a disease, biological state, or physiological condition. A cardiovascular biomarker is often a substance circulating in the blood. Brain natriuretic peptide (BNP): a protein secreted by stretched cardiomyocytes, for instance, when they are hypertrophied after MI. The N-terminal fragment of BNP, called Nt-pro-BNP, is secreted with BNP and is biologically inactive. Both forms can act as biomarkers of HF. Cardiac troponin (cTn): a protein expressed by cardiomyocytes (subtypes I and T) that is released in the blood during cardiac damage. cTns are biomarkers used for the diagnosis of MI. cTns are most often measured with a highly sensitive assay for cTnT (i.e., hs-cTnT). Heart failure (HF): an abnormality of the function or structure of the heart preventing efficient pumping of blood and supply of oxygen and nutriments to the body. HF diagnosis is principally based on BNP and LV ejection fraction. HF occurs in one-fifth of cases in the few months following MI and is associated with a high risk of mortality or rehospitalization. Myocardial infarction (MI): necrosis of a part of the heart due to prolonged ischemia following obstruction of a coronary artery. Two types of MI exist and are characterized by an elevation of circulating levels of cTns: STEMI, which is characterized by ST-segment elevation on the electrocardiogram, and NSTEMI, which is characterized by the absence of ST-segment elevation. Risk stratification: establishment of risk categories. For instance, biomarkers may help to classify MI patients into low/moderate/high risk of developing HF. Sensitivity/specificity: statistical measures used to determine the performance of a test or a biomarker. Sensitivity is the true positive rate, which means the proportion of positives (diseased) correctly identified (have the disease). Specificity is the true negative rate, which means the proportion of negatives (nondiseased) correctly identified (do not have the disease). A biomarker should have sensitivity and specificity as close as possible to 100%.

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miR-144/-451 miR-320

Calcium signaling arrhythmia

HOMER1 NCX1 PPIF CAMK2D VCL CALN

miR-210

miR-199b miR-590

miR-1

miR-214

miR-21 miR-24 miR-212/-132

miR-34 miR-499

miR-34

Inflammaon

Fibrosis/ECM remodeling

Angiogenesis

SEMA4B BCL6

miR-29 miR-378 miR-499 miR-874

miR-34

C-FOS FURIN FBLN2 CTGF SPRED1 SPRY1 COL1A1 COL1A2 COL3A1 FBN1 ELN

miR-101 miR-24 miR-1 miR-210 miR-126

CCNE1 CDC25A GATA2 NOS3 PAK4 ITGA5 EFNA3 CTGF SPRED1 PIK3R2 PAK1

miR-503

miR-212/-132 miR-499 miR-378 miR-199b miR-27b

miR-21 miR-29

miR-133 miR-23a miR-208a

miR-24 miR-143/-145

miR-92a miR-210

miR-22

miR-126

miR-590 miR-199b

RAC1 HSPB6 PTP1B DAPK1 HSPD1 PRKCE PDCD4 BIM FOXO3 PNUTS POFUT1 BCL6 MCL1 MAPK1 CALN CASP8 FOXO3 CALN IGFR1 GRB2 KSR1 MAPK1 DYRK1A PPARG SRF CCND2 RHOA CDC42 NELFA MURF1 THRAP1 MSTN KLF4 KLF5 ADD3 SSH2 MRTF-B PURB HDAC4 SIRT1 CLIC5 HOPX

Apoptosis aging

Differenaon proliferaon hypertrophy

Key: Fibroblast

Endothelial cell

Cardiomyocyte

Macrophage

TRENDS in Molecular Medicine

Figure 1. Regulation of cardiovascular pathways by miRNAs. Representation of some miRNAs involved in key functions of the heart. miRNAs (blue) are expressed by different cell types and target multiple genes (black), which are involved in key functions of the heart (green). Abbreviations: ADD3, Adducin 3; BCL6, B Cell CLL/Lymphoma 6; CALN, calcineurin; CAMK2D, Ca2+/calmodulin-dependent protein kinase II; CASP8, caspase 8; CLIC5, chloride intracellular channel 5; CCND2, cyclin D2; CCNE1, cyclin E1; CDC25A, cell division cycle 25A; CDC42, cell division cycle 42; C-FOS, FBJ murine osteosarcoma viral oncogene homolog; COL1A1, collagen type 1 alpha 1; COL1A2, collagen type 1 alpha 2; COL3A1, collagen type 3 alpha 1; CTGF, connective tissue growth factor; DAPK1, death-associated protein kinase 1; DYRK1A, dual specificity tyrosine phosphorylation-regulated kinase 1A; EFNA3, ephrin A3; ELN, elastin; FBLN2, fibulin 2; FBN1, fibrillin 1; FOXO3, forkhead box O3; GATA2, GATA-binding protein 2; GRB2, growth factor receptor-bound protein 2; HDAC4, histone deacetylase 4; HOMER1, homer homolog 1; HOPX, HOP homeobox; HSPB6, heat shock protein B6; HSPD1, heat shock protein D1; IGFR1, insulin-like growth factor receptor 1; ITGA5, integrin alpha 5; KLF4, Kru¨ppel-like factor 4; KLF5, Kru¨ppel-like factor 5; KSR1, kinase suppressor of ras 1; MAPK1, mitogen-activated protein kinase 1; MCL1, induced myeloid leukemia cell differentiation protein; MRTF-B, myocardin-related transcription factor B; MSTN, myostatin; MURF1, muscle RING finger protein 1; NCX1, sodium–calcium exchanger; NELFA, negative elongation factor complex member A; NOS3, endothelial nitric oxide synthase; PAK 1, serine/threonine-protein kinase 1; PAK 4, serine/threonine-protein kinase 4; PDCD4, programmed cell death 4; PIK3R2, phosphoinositide-3-kinase, regulatory subunit 2; PRKCE, protein kinase C e; PNUTS, protein phosphatase 1, regulatory subunit 10; POFUT1, protein O-fucosyltransferase 1; PPARG, peroxisome proliferator-activated receptor g; PPIF, peptidylprolyl isomerase F; PURB, purine-rich element-binding protein B; PTP1B, protein-tyrosine phosphatase 1B; RAC1, ras-related C3 botulinum toxin substrate 1; RHOA, ras homolog family member A; SEMA4B, semaphorin 4b; SIRT1, sirtuin 1; SPRED1, sprouty-related, EVH1 domain-containing 1; SPRY1, sprouty homolog 1; SRF, serum response factor; SSH2, slingshot protein phosphatase 2; THRAP1, thyroid hormone receptor-associated protein 1; VCL, Vinculin.

fibrosis, neoangiogenesis, and cardiomyocyte hypertrophy (Figure 1). For example, miR-1, one of the four cardiacenriched miRNAs (Box 1), is involved in the development of the heart [9], but also in cardiac fibrosis and cardiac cell apoptosis [10]. The ability of miRNAs to regulate multiple pathophysiological processes is partly due to their

multiplicity of targets. Indeed, they are known to target more than half of protein-coding genes [11]. miRNAs have been detected in biological fluids, including blood [12]. miRNAs detected in the blood, known as ‘circulating miRNAs’, can originate from dying cells, such as necrotic cardiomyocytes following MI, or can be actively 717

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Box 1. Four cardiac-enriched miRNAs: miR-1/-133/-208/-499 The four cardiac-enriched miRNAs, miR-1/-133a/-208b/-499, can be categorized into two groups: miR-1/-133a are ‘muscle-specific’ miRNAs and miR-208b/-499 are ‘cardiac-specific’ miRNAs.  miRNA-1 and miR-133a are clustered on the same chromosomal locus, are expressed in skeletal and cardiac muscle, and are transcribed together as one transcript generating two mature miRNAs. miRNA-1 is the most abundant in the heart. These two miRNAs are both antihypertrophic and are involved in sarcomere organization and signaling cascades [79]. Their expression is decreased in the infarcted heart and increased in the peripheral blood [34]. They are released into circulation very early after tissue injury [34]. Indeed, both miRNAs are increased into the blood of STEMI patients during the first 12 h, and then return to baseline level after 24 h.  miRNA-208 has two isoforms: miR-208a, located in MYH6 genes, and miR-208b, located in MYH7 genes. These two miRNAs are prohypertrophic and are involved in sarcomere organization [79]. They are exclusively expressed in the heart and are increased in the circulation after MI (miR-208b to a higher extent than miR208a) [32,37]. miRNA-499 is located in the intronic region of MYH7B and is involved in myosin gene regulation. This miRNA is highly expressed under normal conditions and is decreased after MI [80], suggesting its release following cardiac damage. Circulating levels of miR-499 are highly increased after MI [37,38].

secreted from living cells [13,14]. When actively secreted from living cells, circulating miRNAs can act as paracrine factors. They are secreted by different cell types and circulate either trapped in exosomes [15], microvesicles [16], or apoptotic bodies [17], or integrated in protein complexes [18], lipids or high-density lipoprotein (HDL) [19]. Thus, they are very stable and resistant to RNAses. The observation that miRNAs are expressed in the heart and are present in the bloodstream led to the suggestion that they may be used as cardiac biomarkers. This review presents recent insights into the role of miRNAs as biomarkers of MI, emphasizing their value as potential tools for personalized healthcare. miRNAs for MI risk stratification Two main risk scores are currently used for evaluating the risk of an individual to develop CVD. The Global Registry of Acute Coronary Events (GRACE) risk score [20] combines age, heart rate, systolic blood pressure, creatinine level, and congestive heart failure (HF) to estimate the risk of death or of developing MI, at admission for an acute coronary event or 6 months later. The Framingham risk score [21] combines age, gender, total and HDL cholesterol levels, smoking habit, systolic blood pressure, and the use of medication to treat high blood pressure, to estimate the 10-year cardiovascular risk of an individual. All the factors taken into account for these risk scores have been shown to be associated with circulating levels of miRNAs (Table 1). The idea that circulating miRNAs and incident MI can be associated is currently emerging, with only a few recent studies aiming to determine the utility of circulating miRNAs for MI risk stratification. In a prospective cohort of 820 participants, 47 patients experienced MI over a 10year follow-up period [22]. Among the 19 miRNAs investigated in the plasma of patients, participants with high levels of miR-126 had a 2.7-fold higher risk of incident MI, and participants with low levels of miR-223 and miR-197 718

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had a high risk of incident MI. miRNA-223 had a stronger association with fatal than nonfatal acute MI, and miR-126 and miR-197 were related to acute MI events that occurred during the first 5 years of the study. The association between these miRNAs and incident MI was stronger than C-reactive protein and other risk factors. The association between miR-126 and the risk of MI is intriguing since miR-126 is a miRNA involved in angiogenesis (angiomiR [23]), and is associated with several cardiovascular risk factors such as diabetes, hyperlipidemia, and age (Table 1). miRNA-197 is abundant in platelets [24] and is also associated with diabetes. miRNA-223, also very abundant in platelets [24], is an oncogenic miRNA that is also involved in inflammation [25] and associated with many cardiovascular risk factors such as hypertension, diabetes, hyperlipidemia, and obesity (Table 1). The observation that these miRNAs are associated with incident MI suggests that their presence in the circulation before MI may reflect an underlying disease such as atherosclerosis. Further studies are required to address the added value of miRNAs to established risk scores used for risk stratification. miRNAs as diagnostic biomarkers of acute MI The diagnosis of MI is based on electrocardiogram (ECG) findings and biomarker levels, principally cTns and more rarely creatine kinase MB. In some cases, the diagnosis of MI can be rendered difficult when, for instance, the ECG is normal and cTn levels are elevated due to a noncardiac cause [3]. Following the discovery of miRNAs in the bloodstream [12,26], it was tempting to speculate that miRNAs could be used as biomarkers for the diagnosis of acute MI. This was strengthened by reports that miRNAs are released from the heart into the circulation upon myocardial injury [27,28]. Animal studies demonstrated that the expression of miRNAs is dynamically and rapidly regulated in the heart after MI [29–32] (Table 2). A recent report showed that circulating levels of muscle-enriched miR-1 and miR133a are increased as early as 15 min after transcoronary ablation of septal hypertrophy, and remained elevated for 4 h [33]. These observations suggested that circulating miRNAs may be very early indicators of myocardial damage. A plethora of small-scale studies in humans reported very encouraging results, suggesting that circulating miRNAs may constitute a novel class of cardiac biomarkers (e.g., [34–38]). Four cardiac-enriched miRNAs were consistently found to be upregulated in the plasma after MI in the majority of studies: miR-208 and miR-499, known as myomiRs since they target b myosin heavy chain (b-MHC) genes, as well as muscle-enriched miR-1 and miR-133. However, subsequent larger-scale studies reported mixed results [39–42]. Small-scale studies Several groups reported, almost simultaneously, that circulating levels of miRNAs are differentially regulated after MI [43–45]. As an example, D’Alessandra and colleagues measured circulating levels of miRNAs in 17 healthy subjects and 33 patients with ST-elevation MI (STEMI) and observed an upregulation of miR-1/-133a/-133b/-499-5p and a downregulation of miR-122 and miR-375 in patients

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Table 1. Association between cardiovascular risk factors and circulating miRNAs miRNAs

Hypertension

Cardiac-related miRNAs No [37,39] miR-1 No [37,39] miR-133a miR-133b miR-208a miR-208b miR-499 AngiomiRNAs miR-let-7e miR-126

No [39] No [39] Yes [55], No [37,39] Yes [55], No [37,39,45] Yes [83] No [37]

Yes [83] miR-296-5p Virus-related miRNAs Yes [83] Hcmv-miR-UL112 Other miRNAs miR-let-7d miR-9 miR-15a Yes [89] miR-17

miR-20 miR-21 miR-23a miR-24 miR-27a miR-28-3p miR-29 miR-30d miR-33 miR-34a miR-92a miR-103 miR-122 miR-124 miR-125a miR-130a miR-146 miR-145 miR-150 miR-151 miR-155 miR-181b miR-183 miR-191 miR-192 miR-195 miR-197 miR-199a miR-210 miR-214 miR-221 miR-222 miR-223 miR-320 miR-328 miR-370

No [37,87] Yes [89] Yes [89] No [37]

Diabetes

Hyperlipidemia

Yes [39] No [39,47]

No [47]

No [39] No [39,47] No [39,55] No [39,45,55]

Yes [84], No [47]

Yes [47] Yes [55], No [55] Yes [55], No [55]

Lack of exercise

Obesity

Smoking habit

Age

Gender

Yes [81] Yes [81], No [82]

Yes [39] Yes [39]

No [39] No [39,47]

Yes [81] No [81] Yes [81]

No [39] No [39] Yes [39], No [55] No [39,55]

No [39,41] Yes [39], No [47] No [39] No [39,47] Yes [39,55] No [39,55]

Yes [39], No [55]

Yes [86], No [47]

No [47]

No [47]

No [47]

No [47]

No [47]

No [47]

No [47]

Yes [47]

Yes [47]

No [47]

No [47]

Yes [55] No [55]

Yes [85], No [47]

No [39] No [39,47] No [39,55]

No [87] Yes [88] Yes [84] Yes [47,90] a Yes [84] Yes [84] No [89] Yes [84] Yes [89] Yes [84] Yes [88], No [84] Yes [88]

Yes [89], No [47] No [87] Yes [89]

Yes [82] Yes [82,87]

No [87] Yes [89]

No [87]

Yes [89] No [87]

No [90] Yes [89] Yes [89] No [37]

Yes [88] Yes [89], No [47] No [89]

No [47,89] Yes [89] Yes [90]

Yes [88] No [87] Yes [89] No [37]

Yes [89] Yes [88] No [47] Yes [84,89]

Yes [89]

Yes [47], No [37]

Yes [47] a

Yes [47]

Yes [89]

No [89] Yes [84] Yes [89] No [89] Yes [84,89] Yes [47] a

Yes [89]

Yes [89]

Yes [82] No [47] Yes [89] No [87]

Yes [81]

Yes [89] Yes [89] Yes [89] No [87]

No [87] No [37] Yes [89]

Yes [89] Yes [89] Yes [89] No [47] No [87]

No [87] Yes [84] Yes [84,89]

Yes [89] Yes [87], No [82] Yes [81] Yes [82] Yes [82,87]

No [87]

No [87]

No [87]

No [87]

Yes [89] No [82] Yes [90] 719

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Table 1 (Continued ) miRNAs miR-375 miR-486 miR-503 miR-509-5p miR-548-3P miR-584 miR-652 miR-940 miR-1292 miR-1323 Panels of miRNAs Panel 1 Panel 2 Panel 3

Hypertension

Diabetes

Hyperlipidemia

Yes [89]

Yes Yes Yes Yes

[88] [84] [91] [89]

Yes [89]

Yes [89] Yes [89]

No [89] Yes [89]

Yes [89] Yes [89]

Lack of exercise

Obesity

Smoking habit

Age

Gender

Yes [89] Yes [92] No [87] Yes [92] Yes [92] Yes [92]

Yes [83] Yes [84] Yes [93]

Panel 1: miR-18b*/-30d/-133b/-296-5p/-324-3p/-486-5p/-516b/-518b/-600/-602/-605/-615-5p/-623/-625*/-634/-664/-1227/-1236/-1249/-1252/let-7e;ebv-miR-BART17-3p/-BART19-5p; hcmv-miR-UL112; kshv-miR-K12-6-3p/-10a/-10b. Panel 2: miR-15a/-16/-20b/-21/-24/-25/-28-3p/-29b/-99b/-122/-125-5p/-126/-134/-139-3p/-139-5p/-140-5p/-146b-5p/-150/-191/-197/-223/-320/-342-3p/-423-5p/-433/-486-5p/-574-3p/885-5p/-let-7b/-let-7e. Panel 3: miR-16/-17/-19a/-19b/-20a/-20b/-21/-24/-25/-26a/-26b/-27a/-29a/-30b/-30c/-92a/-93/-106a/-106b/-126/-126*/-185/-186/-188-5p/-191/-195/-199a-3p/-221/-223/-301a/-328/-3313p/-335/-345/-374a/-374b/-425/-451/-454/-923/-let-7b/-let-7d/-let-7e/-let-7g. Yes, statistically significant association between circulating levels of miRNAs and cardiovascular risk factor (true association or overexpression/underexpression between patients with and without the cardiovascular risk factor); No, no statistically significant association. a

For the comparison between CAD patients and controls. However, in this study, statistical significance was lost when CAD patients with or without diabetes mellitus were compared.

with acute MI compared with controls [34]. Zeller and colleagues measured circulating levels of miRNAs in 29 STEMI and 63 non-coronary chest pain patients and observed an increase of miR-1/-19b/-132/-186/-208a/-208b/210 and a decrease of miR-140-3p in STEMI patients [46]. Other studies confirmed that cardiac-enriched miR1 [35,36], miR-133 [31], miR-208 [32], and miR-499 [37,38] were elevated in the blood of patients with acute MI, and that they were correlated with cardiac biomarker levels. The majority of studies reported robust capacities of circulating levels of cardiac-enriched miRNAs to discriminate patients with acute CVD from healthy controls [47], such as miR-155/-133a/-208b, although none of these miRNAs could outperform cTns for the diagnosis of MI [48]. These studies have been discussed in recent reviews [14,49,50].

Table 2. miRNAs as diagnostic biomarkers of acute MI miRNAs miR-1 miR-21 miR-122 miR-133a/b miR-146a miR-208a/b miR-320a miR-328 miR-375 miR-423-5p miR-499-5p a

Animal studies [34,36]

[34]

Small-scale human studies [31,32,34–36,44,45,48] [45] [34] [31,32,34,44,45,48]

[43]

[37,38,43,44]

[34,43]

[43] [34] [45] [34,37,38,43–45,48]

Large-scale human studies [39,41a,51a] [51] a [39] [51] a [39,40,41a,42a,51a] [42a]

[40,41a,42a,51a]

For studies including patients presenting to the emergency department with chest pain.

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Large-scale studies Following initial small-scale proof-of-concept studies, additional studies in larger patient populations were published. Widera and colleagues compared circulating levels of miR-1/-133a/-133b/-208a/-208b/-499 between patients with unstable angina (UA), STEMI, or non-ST-elevation MI (NSTEMI) in a cohort of 444 patients with CAD [39]. Patients with STEMI and NSTEMI had increased levels of miR-1/-133a/-208b compared with patients with UA. However, large overlaps were observed for these miRNAs between the three groups of patients. miRNA1/-133a/-133b/-208b were associated with levels of highsensitivity cardiac troponin T (hs-cTnT). In a cohort of 597 individuals, containing 397 patients with STEMI, 113 patients with NSTEMI, and 87 healthy subjects, circulating levels of miR-208b and miR-499 measured at presentation showed an increase in detection level in acute MI patients, with higher levels in STEMI patients compared with NSTEMI patients [40]. These two miRNAs were correlated with peak levels of the cardiac markers creatine phosphokinase and hs-cTnT, indicating a correlation with infarct size. miRNA-499 was able to discriminate between patients with acute MI and controls with accuracy comparable to hs-cTnT, but no incremental diagnostic value to hs-cTnT could be demonstrated. Despite their relatively large size, these studies often did not reflect the clinical situation of patients presenting to the emergency department with chest pain. To address this issue, more recent studies evaluating the ability of miRNAs to diagnose MI were performed on populations of patients presenting to the emergency department with suspected CVD. Oerlemans and colleagues measured the circulating levels of miR-1/-21/-146a/-208a/-499 in 332 patients with suspected acute coronary syndrome (ACS) presenting to the

Review emergency department [51]. The five miRNAs were increased in the 106 patients diagnosed with ACS, and a combination of miR-1, miR-499, and miR-21 increased the diagnostic value of hs-cTnT. Gidlof and colleagues measured circulating levels of cardiac-enriched miRNAs miR-1/-208b/499-5p in 424 patients with suspected ACS [41]. Circulating levels of miR-208b and miR-499-5p were higher in MI patients compared with non-MI patients (group containing chest pain and angina) with an increase in STEMI patients compared with NSTEMI patients. These two miRNAs were correlated with cTnT levels, even after adjustment with multiple clinical parameters. These correlations were conserved in the MI group, but were lost in the non-MI group. miRNA-208b had the highest diagnostic value, followed by miR-499-5p and miR-1. However, none of these miRNAs outperformed cTnT. The diagnostic value of six miRNAs (miR-133a/-208b/-223/-320a/-451/-499) was assessed in the large, prospective, multicenter study APACE. A total of 1155 consecutive patients presenting to the emergency department for acute chest pain were enrolled, among which 224 had acute MI [42]. Cardiac-enriched miR-208b/-499 and activated platelet-enriched miR-320a were shown to be elevated in acute MI patients compared with patients with other diagnoses. miRNA-208b had the highest diagnostic accuracy with an area under the receiver-operating characteristic curve of 0.76. Of note, however, none of the miRNAs tested in this study outperformed or added diagnostic value to cTnT or hs-cTnT. This might be explained in part by the fact that the diagnosis of MI was established with hs-cTnT in this population. Many miRNA candidates for diagnosing MI have been identified in small-scale studies, but only a few have been validated in large cohorts of patients. This is mostly due to patient selection modalities and statistical issues in data interpretation from small patient populations. Despite this, a consensus has emerged that it will be very difficult for miRNAs to outperform the diagnostic value of cTns. miRNAs as prognostic tools for acute MI Predicting outcome after MI may be performed with the aid of imaging modalities such as magnetic resonance, which provides information about cardiac dysfunction, and blood biomarkers. The current best prognostic biomarker after acute MI is BNP, particularly its N-terminal pro-form Ntpro-BNP. However, plasma levels of BNP fluctuate after MI, especially anterior MI, thus limiting the accuracy of this biomarker and suggesting the need for serial measurements [52]. In addition, left ventricle (LV) systolic and diastolic function that follows MI is subjected to abrupt changes and the half-life of natriuretic peptides is relatively long. BNP levels are also affected by LV hypertrophy, tachycardia, ischemia, renal dysfunction, age, obesity, and medication. All these confounding factors explain, at least partly, the absence of consensus and recommendations for the use of natriuretic peptides to predict outcome after MI [53]. LV dysfunction is the only predictor of mortality following STEMI. Multiple candidate prognostic biomarkers have been identified (e.g., soluble ST2, midregional proadrenomedullin, neutrophil gelatinase-associated lipocalin) but, as for natriuretic peptides, none of them are recommended for clinical use. Therefore, there is no satisfying post-MI

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prognostic biomarker. Some studies have been designed to investigate the prognostic value of circulating miRNAs after MI. Cardiac-enriched miRNAs Initially identified as potential diagnostic biomarkers of MI, cardiac-enriched miRNAs were the first miRNAs investigated for prognostic purposes. In the study by Widera and colleagues in ACS patients, miR-133a and miR-208b were associated with 6-month all-cause mortality [39]. However, this association was lost after adjustment with hs-cTnT. In the study by Eitel and colleagues in patients with STEMI, miR-133a levels were correlated with prognostic indicators such as infarct size and myocardial salvage index, and patients with miR-133a levels above the median value had an increased risk of a cardiovascular event [54]. However, this study did not address the impact of miR-133a on the predictive value of hs-cTnT and BNP. In a cohort of 500 patients with acute MI, circulating levels of miR-208b and miR-499 were inversely correlated with 4-month ejection fraction (EF) [40]. However, these miRNAs were not significant predictors of shortand long-term mortality [55]. In patients with suspected ACS, miR-208b and miR-499-5p were associated with an increased risk of mortality or development of HF after 30 days, but this association was lost upon adjustment with cTnT [41]. In a recent study on 1155 patients with acute chest pain, miR-208b levels were found to be increased in patients dying within 30 days [42]. However, this was not associated with a strong predictive value for 30-day or 2-year mortality. In elderly NSTEMI patients, miR-499-5p circulating levels were associated with 1-year cardiovascular mortality in a multivariable model, but this association was lost at 2 years of follow-up [56]. According to these studies, cardiac-enriched miRNAs do not outperform current prognostic biomarkers after MI. Non cardiac-enriched miRNAs miRNA-155 and miR-380* were elevated in patients who experienced cardiac death within 1 year after acute MI [57]. However, the prognostic value of these miRNAs in this study remains to be fully established. The temporal profile of circulating levels of miR-1/-21/-29a/-133a/-208 following MI has been characterized and associated with LV remodeling in a small cohort of MI patients [58]. Important variations of miRNA levels were observed between day 2 and day 90 post-MI, suggesting that serial measurements of miRNAs may hold prognostic significance. Circulating levels of the p53-responsive miRNAs miR-192/-194/34a measured at a median of 18 days post-MI showed an increase in detection level in acute MI patients who developed HF within 1 year [59]. In addition, miR-194 and miR34a were correlated with 1-year LV end-diastolic dimension. Using a systems-based approach with networks of miRNA–gene interactions, some miRNAs associated with LV remodeling post-MI were identified [60]. In particular, a group of ten high-traffic miRNAs (i.e., having the highest number of interactions with genes known to regulate LV remodeling) was highlighted. The association between one of these high-traffic miRNAs, miR-150, and LV remodeling 721

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Table 3. miRNAs as prognostic biomarkers of acute MI miRNAs

Refs

miR-16 miR-19a miR-23a miR-27a miR-29a miR-30c miR-31 miR-34a miR-101 miR-126 miR-133a miR-145 miR-150 miR-155 miR-192 miR-194 miR-204 miR-208b miR-223 miR-296-3p miR-380* miR-499 miR-625

[61] [60] [60] [60,61] [58] [62] [60] [59] [60,61] [22] [39,54,60] [62] [60,61] [57,59] [59] [59] [60] [39–42] [22] [60] [57,59] [40,41,56] [60]

Association with poor outcome after MI " a a

" " a

" # " " " # " " " a

" # a

" " a

", Upregulation of the miRNA associated with a poor outcome after MI. #, Downregulation of the miRNA associated with a poor outcome after MI. a

These miRNAs have been identified by interaction network analyses and have not been experimentally validated in patient cohorts.

was validated in two independent cohorts of MI patients. miRNA-150 predicted the variation of end-diastolic volume between discharge and 4-month follow-up with a prognostic value higher than Nt-pro-BNP. A model including both miR-150 and Nt-pro-BNP had an incremental predictive value. Reclassification analyses indicated that miR-150 was able to correctly reclassify half of the patients misclassified by Nt-pro-BNP. The prognostic value of a panel of four miRNAs in 150 MI patients with a 6-month follow-up was investigated. The panel composed of miR-16/-27a/101/-150 increased the prognostic value of Nt-pro-BNP [61]. These results indicated that miRNAs other than cardiac-enriched miRNAs may hold a significant prognostic value in MI patients, and that panels of miRNAs may have a stronger prognostic value than single miRNAs. Accordingly, Meder and colleagues reported that miR145 and miR-30c expressed in peripheral blood cells correlate with infarct size as an estimate of a patient’s risk [62]. The list of miRNAs with potential prognostic value after MI is continuously growing (Table 3). Overall, it appears that noncardiac-enriched miRNAs may have the capacity to increase the value of BNP in outcome prediction after MI; however, additional studies are warranted to confirm this possibility. miRNAs, polymorphisms, and personalized medicine Personalized medicine is ‘an emerging practice of medicine that uses an individual’s genetic profile to guide decisions made in regard to the prevention, diagnosis, and treatment of disease’ (National Institutes of Health; http://www. genome.gov/glossary/). Since the past decade, significant 722

advances have been achieved in personalized medicine in several biomedical domains, particularly in oncology. In cardiology, miRNA pharmacogenomics and the application of miRNAs for personalized medicine are still in a phase of gestation. Nonetheless, genetic polymorphisms in miRNAs or in their target genes have been reported to be associated with CVD. A SNP localized in the 30 UTR of methylenetetrahydrofolate reductase (MTHFR) mRNA modified the binding ability of miR-149, and has been shown to be associated with a higher risk for CVD [63]. A G>A mutation in the 30 UTR of myostatin creates a new target site for miR-1 and miR-206, causing its inhibition and leading to cardiac hypertrophy [64]. An A>C polymorphism (rs5186) in angiotensin receptor 1 (AGTR1) suppresses the binding of miR-155, resulting in downregulation of the expression of the AGTR1 protein, which is involved in hypertension and CVD [65]. Other studies have reported polymorphisms in the genes encoding miRNAs. Polymorphisms in miR-196a2 and miR-499 appear to modulate the occurrence of CAD [66] and its prognosis in a Chinese population [67]. This is interesting since miR-499 has been identified as a potential diagnostic [42] and prognostic biomarker after MI [41]. It is also important to consider the influence of genetic polymorphisms in miRNA seed sequence on miRNA target genes and downstream biological effects. Sequence variation in a 30 UTR of a mRNA affects only one binding site of a family of miRNAs, whereas a mutation in the seed sequence of a miRNA can alter its effect on multiple mRNAs, leading to an increased expression of targets normally degraded, and to a decreased expression of new target mRNAs (i.e., not targeted by the genuine miRNA). For instance, a mutation in the seed sequence of miR-96 has a strong impact on its biogenesis and mRNA targeting, leading to the development of autosomal dominant hearing loss [68]. Even a nonseed mutation can have high impact on miRNA function and organ phenotype, such as a mutation out of the seed sequence of miR-499, which enhances the contractile function of the heart [69]. The effect of these mutations on the biomarker value of miRNAs has been poorly addressed and requires further investigation. From the studies presented here, it is reasonable to believe that miRNAs may have potential clinical applications and may help in the management of patients with MI. miRNAs may be useful for the two aspects of personalized medicine leading to tailor treatment strategy to the individual patient, firstly using miRNA expression profiles, and secondly using genomic data. A goal of personalized medicine is to be able to identify high-risk patients who would benefit most from aggressive treatment and medication. miRNAs able to risk-stratify asymptomatic patients or to predict outcome post-MI have the potential to be used to detect patients at high risk and thus appear as a promising tool for directing patient management. It is also conceivable that miRNAs may be used both as prognostic biomarkers and therapeutic agents. Changes in the circulating levels of miRNAs may also be indicative of patients who would benefit from treatments to either restore deficient amounts of protective miRNAs or inhibit excessive amounts of deleterious miRNAs.

Review Advantages, limitations, and automation The accessibility and stability of miRNAs in biological fluids (i.e., blood, plasma, serum, urine) is undoubtedly an advantage since it allows their measurement in readily available fluids. The sensitivity of PCR used for quantification of circulating levels of miRNAs is also a major advantage since it allows detecting very low copy numbers. For instance, the TaqMan miRNA real-time RT-PCR assay for miR-499 has a detection limit of 240 copies per 100 ml of plasma [38]. This is critical considering that circulating levels of some miRNAs, such as miR-208 [34], can be very low, close to the limit of detection of traditional PCR assays. However, cTns are also measured in the blood, and cTn assays are also highly sensitive. It will be interesting in the near future to compare the sensitivity of nextgeneration miRNA assays and cTn assays, taking into account that excessive sensitivity often leads to false positive discovery. There are still several major limitations to the biomarker value of circulating miRNAs. The presence of comorbidities in study populations is important, and circulating levels of miRNAs might be affected by multiple cardiovascular risk factors, which must be considered as confounding factors in multivariable prediction analyses (Table 1). It is also crucial to consider the impact of medication on circulating levels of miRNAs. This is particularly important in the context of MI, where patients often take several drugs before and after the event. Statins, inhibitors of the renin–angiotensin system, and aspirin affected circulating levels of miR-17/-145/-155 in two cohorts of CAD patients and controls [47]. Recently, antiplatelet therapy was shown to modify circulating miRNAs by decreasing platelet miRNAs (miR-223 and miR-191), and also other miRNAs such as miR-126 and miR-150 [24]. Aspirin has also been shown to reduce circulating levels of miR-126 [70]. Of note, these observations suggest that circulating miRNAs might aid to identify responders to medication from nonresponders, and therefore may become a very useful tool for personalized medication strategies and surveillance of response to treatment. A technical obstacle to miRNA measurements is generated by heparin, which when used during blood sampling or when administrated to patients with CVDs strongly interferes with PCR [71]. It is also important to consider the kinetics of miRNA release into the blood [33]. Time-dependent variations in the concentration of miRNAs in the bloodstream may explain, at least to some extent, the lack of reproducibility of findings between studies. The robustness of a biomarker relies heavily on its ease of detection, stability, level of expression, and disease specificity. The value of a miRNA biomarker is limited by the duration of the assay used to assess miRNA expression. While cTns can be measured at the point-of-care in less than 10 min, the PCR method used to assay miRNAs is time-consuming and fastidious. This method, which is still the gold standard for quantification of circulating miRNAs, is not yet fully standardized. The normalization method used in miRNA assays is also a critical issue. To date, no housekeeping miRNAs have been reliably and reproducibly identified. Furthermore, normal reference ranges for circulating levels of miRNAs will have to be determined. Also, further studies are required to

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define standardized protocols for miRNA assays, from clinical sample collection, processing, and eventually storage, to miRNA assessment. Efforts are underway to develop automated systems for a rapid and sensitive quantification of miRNAs in the blood. This is one of the bigger challenges in translating miRNA biomarkers into clinical application. Classical molecular techniques such as quantitative RT-PCR, microarrays, or northern blot are limited by a sample preparation step and by their normalization procedure [72,73]. New methods based on biosensors have been developed. There are three types of miRNA biosensors: electrochemical [74], electromechanical [75], and optical [76], characterized by the combined use of a classical molecular probe with a high-sensitive transducer design in sensors [77]. These methods present several advantages such as a high reliability of measurement, an absence of amplification step, and a low time to results. In 2011, Roy and colleagues reported the implementation of a microfluidicassisted microarray allowing ultrasensitive detection of miRNAs [78]. Performed under an optical microscope, this assay does not require an amplification step, thus reducing assay time to 1 h for total RNA samples. In addition, the assay is highly sensitive, with a lower detection limit close to 300 copies/ml. Among commercial systems to measure circulating miRNAs, the nCounter1 miRNA Expression Assay (Nanostring Technologies) is a multiplex fully automated system that also avoids amplification but requires total purified RNA as input. The main advantage of this system resides in its capacity to measure 800 targets simultaneously, allowing for multimarker approaches. Another system able to measure the expression of several miRNAs simultaneously is the HTG Edge System (HTG Molecular Diagnostics Inc.). This system does not require extraction and amplification steps, can be used for both cellular and plasma samples, and can measure up to 47 miRNAs in less than 24 h, in an automated manner. This system is based on a trapping of miRNAs on a plate, and detection by chemiluminescence. It is expected that automated systems for miRNA measurement will soon be developed for use as point-of-care tests, and that these may compete with high-sensitivity cTn assays. Concluding remarks The studies presented in this review support the concept that circulating miRNAs may be valuable for risk stratification and as prognostic biomarkers. Concerning their diagnostic value, although cardiac-enriched miRNAs are detectable in the bloodstream very rapidly after MI, it will be difficult to outperform cTns for the diagnosis of MI. miRNAs may be more useful for prognostication purposes. Indeed, recent reports indicate that circulating levels of miRNAs hold some prognostic value after MI [57–59] and improve the diagnostic value of BNP [60,61]. More prospective and large cohort studies are needed to validate the utility of miRNAs as prognostic biomarkers after MI. Unlike most past studies, future studies will have to systematically consider the impact of comorbidities, cardiovascular risk factors, and medication on circulating 723

Review Box 2. Outstanding questions  Will miRNAs be useful for prognosis after MI?  Will routinely applicable miRNA assays be developed? What will be their sensitivity and specificity?  Will miRNA assays be cost-effective?  Will miRNA-based personalized medicine improve patient outcome?

levels of miRNAs. Overall, while recent studies have shown the potential utility of circulating miRNAs as cardiac biomarkers, no candidate miRNAs have reached clinical application yet. The use of miRNAs for personalized healthcare of MI patients faces important limitations and several key questions will have to be answered before they can be considered for clinical application (Box 2). Recent avenues, notably in the oncology field, support the concept that finding a miRNA, or most probably a panel of miRNAs, that could be used at the same time to diagnose, predict outcome, treat, and monitor treatment response would be a significant achievement. Early identification of patients at risk of developing HF post-MI, and who would mostly benefit from aggressive therapy would be a major advance. However, there are at the present time scarce data demonstrating the usefulness of miRNAs in cardiology. Further efforts are warranted in this respect. Acknowledgments We thank Lu Zhang for help with figure design.

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miRNAs as biomarkers of myocardial infarction: a step forward towards personalized medicine?

miRNAs are small noncoding RNAs known to post-transcriptionally regulate gene expression. miRNAs are expressed in the heart where they regulate multip...
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