Literature DB >> 29688487

Circulating non-coding RNAs in biomarker-guided cardiovascular therapy: a novel tool for personalized medicine?

David de Gonzalo-Calvo1,2,3,4, Angela Vea1, Christian Bär3, Jan Fiedler3, Liam S Couch3,5, Carlos Brotons6, Vicenta Llorente-Cortes1,2,4, Thomas Thum3,5,7.   

Abstract

Current clinical guidelines emphasize the unmet need for technological innovations to guide physician decision-making and to transit from conventional care to personalized cardiovascular medicine. Biomarker-guided cardiovascular therapy represents an interesting approach to inform tailored treatment selection and monitor ongoing efficacy. However, results from previous publications cast some doubts about the clinical applicability of biomarkers to direct individualized treatment. In recent years, the non-coding human transcriptome has emerged as a new opportunity for the development of novel therapeutic strategies and biomarker discovery. Non-coding RNA (ncRNA) signatures may provide an accurate molecular fingerprint of patient phenotypes and capture levels of information that could complement traditional markers and established clinical variables. Importantly, ncRNAs have been identified in body fluids and their concentrations change with physiology and pathology, thus representing promising non-invasive biomarkers. Previous publications highlight the translational applicability of circulating ncRNAs for diagnosis and prognostic stratification within cardiology. Numerous independent studies have also evaluated the potential of the circulating non-coding transcriptome to predict and monitor response to cardiovascular treatment. However, this field has not been reviewed in detail. Here, we discuss the state-of-the-art research into circulating ncRNAs, specifically microRNAs and long non-coding RNAs, to support clinical decision-making in cardiovascular therapy. Furthermore, we summarize current methodological and conceptual limitations and propose future steps for their incorporation into personalized cardiology. Despite the lack of robust population-based studies and technical barriers, circulating ncRNAs emerge as a promising tool for biomarker-guided therapy.
© The Author(s) 2018. Published by Oxford University Press on behalf of the European Society of Cardiology.

Entities:  

Keywords:  Biomarker ; Cardiovascular disease ; Long non-coding RNAs ; MicroRNAs ; Non-coding RNAs ; Personalized medicine ; Precision medicine

Year:  2019        PMID: 29688487      PMCID: PMC6528150          DOI: 10.1093/eurheartj/ehy234

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


Biomarker-guided cardiovascular therapy

Recent decades have seen advances in the treatment of cardiovascular disease (CVD) due to the development of highly effective and well-tolerated therapeutic interventions., Despite this, CVD persists as the leading cause of death worldwide (http://www.who.int/cardiovascular_diseases). This is in part due to the interindividual variability in the response to medical treatments. The variability in therapeutic outcomes differs according to demographic, clinical, environmental, and genetic factors., Nevertheless, therapy is rarely tailored, as evidence-based medicine assumes a uniform patient phenotype. Several authors have proposed to incorporate molecular profiling into clinical decision-making to allow patients, or subgroups of patients, to receive precision medical care., Tailored therapies based on a refined understanding of patient’s molecular characteristics could be administered to those who are anticipated to benefit most, whereas simultaneously limiting ineffective or harmful interventions., This would ultimately lead to improved clinical outcomes, increased patient quality of life, and better allocation of health care resources. In this context, utilizing biomarkers to objectively assist in individualized treatment has gained attention in recent years. Indeed, drug-diagnostic co-development (‘companion diagnostics’) has become increasingly important in the pharmaceutical industry. Biomarker-guided therapy, the use of biomarkers to tailor medical treatment, could assist in: (i) selection of therapy, (ii) evaluation of efficacy, (iii) optimal dose selection, and (iv) recognition and avoidance of side-effects (Figure ). Reflecting this, previous studies have illustrated the usefulness of circulating biomarkers to tailor cardiovascular therapy. Most notably, N-terminal prohormone of brain natriuretic peptide (NT-proBNP)-guided treatment has been suggested to reduce or delay cardiovascular outcomes and to improve cost-effectiveness compared to guideline-based therapy. Despite promising findings, other data cast doubts as to whether circulating biomarkers can support clinical decision-making. Genome sequence variations, including single nucleotide polymorphisms (SNPs), could act as biological markers of the response to certain drugs. Nevertheless, genomics alone provide an incomplete picture of the patient’s phenotype. Thus, there is a clinical need for innovative biomarkers to tailor cardiovascular therapy. Biomarker-guided therapy.

Non-coding RNAs, an emerging concept in disease and therapy

Although 80% of the human genome is transcribed, only 1–3% of all transcripts are further translated into proteins (Figure ). The vast majority of the human transcriptome represents non-coding RNAs (ncRNAs) (Figure ), which play key regulatory functions., Contrary to the number of protein-coding genes, which is relatively static since the evolution of multicellularity, the amount of ncRNAs has continued to increase along with the complexity of the organism. This suggests a fundamental role of ncRNAs in the evolution of biological complexity. Coding and non-coding transcriptome. Although approximately 80% of the human genome is transcribed, only 1–3% of this corresponds to protein-coding nucleotide sequences (messenger RNAs, green circles). The remaining 97–99% corresponds to RNAs not translated into proteins: non-coding RNAs (orange), except for some specific exceptions. The non-coding RNA transcriptome includes: circular RNA (circRNAs), enhancer RNAs (eRNAs), germline small RNAs (gsRNAs), long non-coding RNAs (lncRNAs), microRNAs (miRNA), piwi-interacting RNAs (piRNAs), ribosomal RNAs (rRNAs), ribozymes, small conditional RNAs (scRNAs), small interfering RNAs (siRNAs), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), transfer RNAs (tRNAs), and Y RNA (YRNA). The symbols used are merely indicative. Non-coding transcripts participate in most cellular processes, and therefore, play a causative role in a number of diseases., Accordingly, the manipulation of ncRNA expression has emerged as a novel and promising therapeutic approach., Of note, clinical trials have evaluated the safety, efficacy, and tolerability of ncRNAs in the treatment of various diseases, such as chronic hepatitis C (www.clinicaltrials.gov, NCT01200420) and kidney disease (www.clinicaltrials.gov, NCT02855268). Excellent publications have recently reviewed the role of ncRNAs as potential therapeutic targets in CVD,,, and therefore this issue is beyond the scope of this review. Since the great majority of investigations are in pre-clinical stages, further efforts are needed to demonstrate the clinical safety and efficacy of ncRNAs in cardiovascular therapy.

Circulating non-coding RNAs

In addition to the relevance of ncRNAs as targets or tools for therapy, a plethora of reports suggest that the transcriptomic signature, particularly from ncRNAs, could give insights into both physiological and pathological states of a patient. Indeed, transcriptome-originating biomarkers may provide a deep characterization of disease phenotypes and could offer information that is not captured by traditional markers or clinical characteristics. Despite this, only a small percentage of the non-coding transcriptome has been explored for this purpose. ncRNA expression exhibits high variations between cells, is highly dynamic and rapidly alters in response to stressors. Indeed, a close correlation between dysregulation of the ncRNA profile and the pathophysiology of numerous cardiovascular conditions has been demonstrated. Furthermore, ncRNA alterations could be indicative of complex interaction between genetic and environmental factors. Diet, exercise, biological rhythms, or other exogenous factors not routinely considered during clinical evaluation influence ncRNA expression. Therefore, the non-coding transcriptome provides an interesting opportunity for the identification of novel tools to assist in clinical decision-making. Supporting their potential as easily accessible and minimally invasive biomarkers, the presence of ncRNAs has been extensively described in a large number of body fluids. From a clinical perspective, the non-coding transcriptome, in particular microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have the optimal biochemical properties to be excellent biomarkers. Indeed, miRNAs and lncRNAs are stable against degradation, have a long half-life in biological samples and can be obtained by standard laboratory techniques., RNA can be quantified with high sensitivity and specificity through quantitative reverse transcription-polymerase chain reaction (RT-qPCR), a technique readily available in clinical laboratories. In comparison to peptide biomarkers, ncRNAs can be quantified even at low amounts. Furthermore, global profiles can be obtained in a single experiment using RT-qPCR panels, next-generation sequencing or microarrays. Most studies on circulating ncRNAs as cardiovascular biomarkers are focused on the potential clinical application of miRNAs and lncRNAs in the diagnosis of disease and assessment of cardiovascular risk. These approaches provide valuable information on patient selection for given therapies (Figure ). However, the role of ncRNAs as diagnostic and prognostic biomarkers is beyond the interest of the current review and has been extensively reviewed elsewhere., Treatment selection and monitoring are key aspects of medical management (Figure ). Based on their biological characteristics, the ncRNA profile may reflect the molecular configuration required for any intervention to exert its therapeutic effect, providing an innovative approach for treatment allocation. Additionally, the post-baseline changes in the ncRNA signature may be useful to monitor ongoing effectiveness. Despite a number of publications have addressed these issues in the cardiovascular context, this research field has not been reviewed in detail. Here, we summarize the current knowledge on ncRNAs, specifically miRNAs and lncRNAs, as biomarkers to predict and monitor the response to cardiovascular treatment. Potential clinical application of circulating non-coding RNAs in personalized cardiovascular medicine.

Circulating microRNAs in biomarker-guided cardiovascular therapy

miRNAs are a group of small ncRNAs (19–25 nucleotides in length) that regulate gene expression at the post-transcriptional level. Approximately 2500 human miRNAs have been annotated in the miRBase database (http://www.mirbase.org). miRNAs are predicted to target >60% of protein-coding genes, and play a key role in development, homeostasis, and response to stress., miRNAs are usually expressed in complex networks that enable subtile control of the cellular phenotype. In addition to their intracellular localization, miRNAs have also been described in the extracellular milieu. Extracellular miRNAs are protected from degradation by packaging in lipid vesicles and by binding to proteins or lipoproteins., Extracellular miRNAs participate in intercellular communication at autocrine, paracrine, and endocrine levels., They have been implicated in both physiological and adaptive responses, as well as in the onset and development of disease states, including in CVD. Results from a number of publications support the promising role of circulating miRNAs in the prediction of the therapeutic response and monitoring the effectiveness of treatment (Table ). Studies focused on circulating microRNAs in biomarker-guided therapy ACE, angiotensin-converting enzyme; ACS, acute coronary syndrome; CPAP, continuous positive airway pressure; CHD, coronary heart disease; CRT, cardiac resynchronization therapy; CVD, cardiovascular disease; HF, heart failure; LVAD, left ventricular assist device; NSTE-ACS, non-ST elevation acute coronary syndrome; OSA, obstructive sleep apnoea; PDW, platelet distribution width; RH, resistant hypertension; T2DM, type 2 diabetes mellitus.

Antiplatelet therapy

Antiplatelet therapy is the cornerstone treatment in patients at high risk of thrombosis. Since resistance to the therapy has been associated with higher incidence of cardiovascular events, non-responder patients may benefit from alternative antiplatelet therapies. The development of robust laboratory tests to monitor response to antiplatelet therapy is thus of clinical importance. However, the current methodology has provided varying results. Results from different studies suggest the potential of circulating miRNA levels as surrogate biomarkers of antithrombotic therapy efficacy. In 2013, de Boer et al. demonstrated that aspirin administration (300 mg/day) resulted in a significant reduction in circulating levels of miR-126 in patients with type 2 diabetes mellitus (T2DM) and without a history of CVD after 6 weeks of treatment. miR-126 levels also showed a direct correlation with platelet activation following treatment, irrespective of dose. Indeed, aspirin inhibited in vitro transfer of platelet-enriched miRNAs from the platelets to the extracellular milieu. The potential of circulating miR-126 to monitor aspirin therapy was corroborated by Willeit et al. who evaluated a panel of 92 miRNAs in plasma of healthy volunteers after treatment with 10 mg prasugrel, followed by combination therapy with varying aspirin doses in successive weeks. Plasma levels of 15 circulating miRNAs, including miR-126 and other platelet-enriched miRNAs (miR-191 and miR-223), decreased with further platelet inhibition. These findings were validated in patients with symptomatic carotid artery atherosclerosis under treatment with dipyridamole or clopidogrel, in addition to aspirin. The same group demonstrated that circulating miRNAs are closely correlated with platelet activation biomarkers in general population. The circulating signature of miRNAs was also associated with the residual platelet activity in patients with acute coronary syndrome on antiplatelet therapy. It is worth of noting that the authors suggested a role for the small ncRNA family, YRNAs, as novel biomarkers to monitor antiplatelet therapy. Interestingly, a score based on the plasma miR-92a levels and the platelet distribution width (PDW), can identify aspirin resistance post-baseline with a specificity of 97.5% and a sensitivity of 80.0% in patients with intermittent claudication on daily aspirin monotherapy (75 mg/day, 100 mg/day, or 150 mg/day). Results from these studies were validated in alternative sources of miRNAs. Kok et al. performed miRNA microarray analysis using platelet RNA obtained from a small cohort of healthy male subjects without personal history of CVD. Results showed that altered expression of miR-19b-1-5p, miR-548e, miR-587, miR-1225-3p, miR-1271, and miR-1537-5p correlated with the extent of platelet aggregation reduction after 2 weeks of aspirin therapy (100 mg/day). Lower platelet miR-19b-1-5p expression after aspirin therapy was associated with platelet aggregation in the presence of indomethacin, which mimics the effect of aspirin. As such, miR-19b-1-5p may be a suitable biomarker to identify aspirin insensitivity. It should be noted that a previous publication demonstrated that the platelet miRNA profile is remarkably stable and is not affected by a single-dose of aspirin in a small healthy cohort. However, single-dose treatment may not have an immediate effect on platelet miRNAs. Shi et al. explored the association between platelet miRNA expression and the responsiveness to a clopidogrel loading dose (300 mg) in a cohort of non-diabetic coronary heart disease patients with non-ST elevation acute coronary syndrome (NSTE-ACS). Logistic regression analyses demonstrated that among a number of factors potentially associated with platelet reactivity (age, obesity, smoking, genotype, and other medication), miR-223 expression was an independent predictor associated with clopidogrel responsiveness [Odds ratio (OR) 0.189, 95% Confidence Interval (CI) 0.043-0.836]. Plasma miR-223 showed a clinically acceptable discriminative value [Area Under the Curve (AUC) 0.706, 95% CI 0.521-0.891] and the association with clopidogrel responsiveness remained significant even after adjusting for CYP2C19*2 (OR 0.193, 95% CI 0.044–0.855), a genetic polymorphism that influences the response to clopidogrel. These observations suggest that miRNAs are able to reveal measurable changes in platelet activation and aggregation after antiplatelet therapies and may serve as indicators of compliance in both general and specific patient populations. However, due to the high incidence of suboptimal responses and the number of alternatives in antiplatelet therapy, the further development of novel biomarkers to predict the response to the treatment seems fundamental.

Cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) has emerged as a well-established therapy for advanced heart failure (HF). However, a significant number of patients do not show objective evidence of a clinical benefit. Implantation of a CRT device is invasive and has high costs. Therefore, there is great interest in identifying patients who are most likely to benefit. Melman et al. evaluated the role of circulating miRNAs as biomarkers to predict the response to CRT in a cohort of patients referred for CRT pacemaker implantation with and without subsequent echocardiographic improvement after 6 months. Responders were defined as individuals with an increase in left ventricular ejection fraction (LVEF) ≥ 10% between baseline and follow-up transthoracic echocardiography. After an initial discovery phase using a 766 miRNA array and a subsequent validation phase, plasma miR-30d was identified as a predictor of therapeutic response in multivariate models, even after adjusting for serum creatinine, previous revascularization, preimplantation QRS duration, and LVEF (OR 2.52, 95% CI 1.07–5.94). The discriminative ability was superior to clinical variables such as QRS duration in univariate logistic regression analyses (OR 2.32, 95% CI 1.16–4.67 vs. OR 1.00, 95% CI 0.82–1.34). Thus, preimplantation circulating miR-30d could be a biomarker for the selection of CRT or alternative medical interventions. Interestingly, using in vitro and in vivo models, the authors demonstrated that miR-30d mediates cardiac hypertrophy and protection against inflammation-induced cardiomyocyte apoptosis. Marfella et al. evaluated 84 miRNAs previously associated with the pathogenesis of the failing heart in 81 patients with HF eligible for CRT. At 12 months, patients were categorized as responders if they exhibited LV reverse remodelling [reduction >10% in LV end-systolic volume (LVESV) index and an increment of >10% in LVEF] or non-responders (reduction <10% in LVESV index and an increment of <10% in LVEF). In contrast to previous findings, similar levels of circulating miRNAs were observed between responders and non-responders at baseline, which makes the potential for patient stratification by preimplantation miRNA levels unclear. However, the authors reported higher expression levels of circulating miR-26b-5p, miR-29a-3p, miR-30e-5p, miR-92a-3p, and miR-145-5p after 12 months of CRT in responders (fold-changes from 15.2 to 6.2) compared to non-responders (fold-changes from 5.6 to 2.1). These miRNAs were directly associated with changes in LVEF and NT-proBNP, even after adjusting for age, gender, functional class, and treatments. Thus, post-baseline circulating miRNA levels may be useful to assist in monitoring of efficacy of CRT.

Left ventricular assist devices

The use of LV assist devices (LVAD) in patients with advanced HF is limited by high rates of adverse events and cost., Various clinical variables have been studied to select patients based on anticipated therapeutic response. However, appropriate patient stratification remains challenging. The potential of circulating miRNAs to predict the response to LVAD has been previously evaluated in patients with severe advanced HF. Morley-Smith et al. classified the clinical response to LVAD therapy based on NT-proBNP concentration at 3 months. Selected patients whose change in NT-proBNP was above the 50th percentile were identified as ‘poor responders’ and patients below the 50th percentile as ’good responders’. Using a screening approach including 1113 miRNAs, the authors demonstrated that miR-1202 was a useful biomarker to predict the response to LVAD therapy. Baseline circulating miR-1202 levels stratified the cohort into poor and good responders with greater accuracy than baseline NT-proBNP (AUC 0.976, 95% CI 0.904–1.000 vs. AUC 0.071, 95% CI 0.000–0.210). Thus, miR-1202 emerges as an interesting biomarker to select LVAD therapy or alternative interventions, such as urgent cardiac transplantation. Within the same study, circulating miR-483-3p levels were upregulated following intervention, with >2-fold-change from baseline even after 3 months. This up-regulation mirrored the improvement in LV function. Other studies similarly support the alteration in the circulating miRNA signature after LVAD implantation., Since the evaluation of the changes in ventricular function after LVAD implantation has limitations, serial measurements of the circulating miRNA signature could be an interesting and easily accessible tool to complement the current methodology for monitoring the therapeutic response to LVAD.

Antihypertensive therapy

Efforts have been made to predict and monitor antihypertensive therapy using circulating miRNAs. Sánchez-de-la-Torre et al. analysed a panel of 84 miRNAs functionally associated with the cardiovascular system in plasma samples from 38 patients with resistant hypertension (RH) and obstructive sleep apnoea (OSA) at baseline and after 3 months of adherent continuous positive airway pressure (CPAP) use. Pre-treatment levels of a cluster of miRNAs, miR-100-5p, miR-378-3p, and miR-486-5p, discriminated between non-responders and patients with a favourable decrease in blood pressure (AUC 0.92, 95% CI 0.79–0.99). Supporting previous results from different medical treatments, the changes in plasma miRNA profile after the treatment differed between responders and non-responders. Notably, the authors developed a predictive screening algorithm (HIPARCOScore) based on the specific cluster of miRNAs to identify those responders. Dickinson et al. demonstrated that circulating miRNAs are biomarkers of therapeutic efficacy in different hypertension-induced heart disease rat models. Although the clinical application of the results is limited due to the lack of patient-based data, this study provides interesting conceptual implications. Different miRNA profiles were observed in response to established and experimental therapies; angiotensin-converting enzyme (ACE) inhibitor (captopril) and antimiR-208a treatment. The results support the potential of circulating miRNAs as specific treatment biomarkers. Indeed, circulating levels of miR-19b only changed in response to antimiR-208a therapy. Furthermore, levels of circulating miRNAs correlated with the expression of the myosin isoform Myh7, suggesting that miRNAs could be an interesting tool to monitor the improvement in cardiac function.

Circulating long non-coding RNAs in biomarker-guided cardiovascular therapy

lncRNAs comprise a vast family of ncRNAs longer than 200 nucleotides that participate in multiple biological processes, including imprinting, scaffold/guide for epigenetic and transcription factors, transcription activation, RNA splicing, nuclear trafficking, and regulation of the miRNA function., Several lines of evidence indicate that lncRNAs play an essential role in development, homeostasis, and disease. lncRNAs exhibit cell type-specific expression, precise subcellular localization, and dysregulated expression in pathological conditions. Around 28 000 human lncRNAs have been annotated according to the GENCODE project. Compared to miRNAs, the characterization of extracellular lncRNAs is poor. The precise mechanism of lncRNA release into the extracellular space and their role in intercellular communication is not yet understood. Despite this, lncRNAs have also been detected in stable form in extracellular fluids. Circulating lncRNAs, such as the long intergenic non-coding RNA predicting cardiac remodelling (LIPCAR), are robust biomarkers of cardiovascular conditions., However, the literature pertaining to the role of circulating lncRNAs as biomarkers to tailor cardiovascular therapy is scarce. In contrast, oncological research supports the use of circulating lncRNAs in biomarker-guided therapy. Our group has recently proposed the potential of circulating lncRNA as blood-based biomarkers to guide pioglitazone therapy. After analysing a panel of lncRNAs related to cardiovascular pathology in a cohort of patients with well-controlled T2DM, we reported that pre-treatment levels of the circulating lncRNA smooth muscle and endothelial cell-enriched migration/differentiation-associated long non-coding RNA (SENCR) are positively associated with the rate of change in diastolic function after 24 weeks of pioglitazone therapy. Of note, levels of circulating SENCR before pioglitazone therapy allowed the discrimination of responders from non-responders (AUC 0.763, 95% CI 0.558–0.967). Thus, pre-treatment levels of circulating SENCR may constitute a clinically useful biomarker for the identification of patients that will benefit from pioglitazone administration (Figure ). Potential clinical application of the long non-coding RNA smooth muscle and endothelial cell-enriched migration/differentiation-associated long non-coding RNA (SENCR) in patient stratification to pioglitazone therapy.

Limitations and perspectives

The reproduction of current results in independent and multicentre studies with large population sizes will be a key step to obtain convincing evidence for clinical applicability. Only few overlapping ncRNAs on similar therapies have been described, possibly due to the great heterogeneity in the selection of patients and the evaluation of numerous therapeutic responses. Another aspect to be considered is the inadequate control for confounders. Circulating levels of ncRNAs are affected by factors such as age, sex, cardiovascular risk factors, and pharmacological treatments. These should be considered in the interpretation of results. The effects of disease stage and subtype on ncRNA expression are additional sources of variation that should be similarly considered. The use of ncRNA clustering may provide higher accuracy than single ncRNAs. The inclusion of novel members of the non-coding transcriptome that exhibit a great potential to be used as biomarkers, such as circular RNAs (circRNAs), is also required. Unfortunately, a remarkable proportion of published studies have not compared patterns of circulating ncRNAs with established indicators (e.g. peptide biomarkers). Furthermore, most studies have evaluated the univariate association between ncRNAs and treatment response. It is fundamental to explore whether the addition of specific ncRNAs, or a signature of diverse ncRNAs, to composite scores based on multiple parameters from different methodologies provide greater characterization and prediction of patient responses. Advances in ‘Big Data’ should be utilized to combine the complexity of clinical, biochemical, genomic, transcriptomic variables, in addition to novel pieces of information such as the environment and the microbiome, to maximize prediction. Ongoing European consortia such as HOMAGE (http://www.homage-hf.eu/) will together identify the role of circulating ncRNAs in thousands of CVD patients. Ischaemic heart disease deserves particular attention. Despite myocardial ischaemia/reperfusion injury and ischaemic HF being the main causes of morbidity and mortality in industrialized societies, the number of publications that have addressed the potential clinical application of ncRNAs in biomarker-guided therapy is scarce, and merely focused on platelet activation and aggregation., Further studies, should evaluate whether ncRNAs are useful tools to inform tailored treatment selection and monitor ongoing efficacy in alternative treatments. This is particularly important in the context of the new expensive monoclonal antibodies targeting PCSK9 (evolocumab and alirocumab) and interleukin-1β (canakinumab). Despite having promising biochemical properties for use as biomarkers, implementation of circulating ncRNAs is currently not feasible in all clinical laboratories. ncRNA quantification is laborious with current technology and is highly dependent on specimen collection, storage, and handling. As such, ncRNAs quantification and data processing is highly variable between studies. There is a crucial need to develop guidelines of best practice and standard operating procedures in order to increase consistency among results and to make studies comparable and reproducible. Furthermore, the addition of ncRNA testing to clinical practice would currently be time-consuming and may delay commencement of therapy. The development of automated and standardized assays for clinical application should thus be intensified. Finally, evaluation of the cost-effectiveness of circulating ncRNAs in comparison to current methods must be undertaken to assess future viability.

Concluding remarks

A blood test based on circulating ncRNAs would constitute a promising tool to assist physician decision-making in cardiovascular therapy (Figure ). Nevertheless, since most studies are still in pre-clinical phases, additional research is needed to clinically adopt the non-coding transcriptome for personalized medicine.
Table 1

Studies focused on circulating microRNAs in biomarker-guided therapy

TherapyPatients/Subjects/ModelSourceMain findingsReferences
AspirinPatients with T2DM and without a history of CVDPlasmaTherapy reduces circulating miR-126 levels 49
AspirinHealthy subjectsPlasmaTherapy reduces the circulating levels of a panel of miRNAs, including platelet-enriched miRNAs 50
Patients with symptomatic carotid atherosclerosis
AspirinHealthy volunteersPlasmaInterindividual variability in circulating miRNA profile decreased with prolonged therapy 51
Patients with a history of ACSPlatelet-related miRNAs correlated with platelet function tests
Bruneck cohortPlatelet-related miRNAs correlated with platelet activation markers
AspirinPatients with intermittent claudicationPlasmaCombination of circulating miR-92a and PDW is a predictor of aspirin resistance 52
AspirinHealthy subjects without history of CVDPlateletsChanges in expression levels of miR-19b-1-5p, miR-548e, miR-587, miR-1225-3p, miR-1271, and miR-1537-5p correlate with a reduction in platelet aggregation 53
Platelet miR-19b-1-5p expression is associated with aspirin insensitivity
AspirinHealthy subjectsPlateletsNo change in miRNA expression 54
ClopidogrelCHD patients with NSTE-ACSPlateletsPlatelet miR-223 expression is a predictor of clopidogrel responsiveness 55
CRTPatients with HF eligible for CRTSerumNo differences in miRNA expression profile between responders and non-responders at baseline 56
Different changes in circulating miRNA between responders and non-responders after the therapy
At follow-up, circulating miR-26b-5p, miR-29a-3p, miR-30e-5p, miR-92a-3p, and miR-145-5p were differentially expressed between responders and non-responders
CRTPatients with HF referred for CRTPlasmaHigher levels of circulating miR-30d, miR-142-5p, and miR-766 in responders at baseline 57
Association between circulating miR-30d levels and CRT responsiveness
Reduction in circulating miR-30d levels at 6 months in responders
LVADPatients with severe advanced HFPlasmaCirculating miR-483-3p monitors the response to LVAD therapy 58
Circulating miR-1202 predicts the response to LVAD therapy
LVADPatients with end-stage HFPlasma / SerumReduction in circulating levels of heart-enriched miRNAs in response to LVAD therapy 59
LVADPatients with end-stage HFPlasmaCirculating miR-21 decreased at 1, 3, and 6 months after LVAD implantation 60
AntihypertensiveHypertension-induced heart disease rat modelsPlasmaReduction of circulating levels of miR-16, miR-20b, miR-93, miR-106b, miR-223, and miR-423-5p in response to antimiR-208a and ACE inhibitor therapies 61
Circulating miR-19b could serve as a specific biomarker for antimiR-208 therapy
CPAPPatients with RH and OSAPlasmaA cluster of circulating miRNAs (miR-100-5p, miR-378-3p, and miR-486-5p) predict blood pressure response 62
Different profile in miRNA changes between responders and non-responders

ACE, angiotensin-converting enzyme; ACS, acute coronary syndrome; CPAP, continuous positive airway pressure; CHD, coronary heart disease; CRT, cardiac resynchronization therapy; CVD, cardiovascular disease; HF, heart failure; LVAD, left ventricular assist device; NSTE-ACS, non-ST elevation acute coronary syndrome; OSA, obstructive sleep apnoea; PDW, platelet distribution width; RH, resistant hypertension; T2DM, type 2 diabetes mellitus.

  80 in total

1.  Warfarin genotyping reduces hospitalization rates results from the MM-WES (Medco-Mayo Warfarin Effectiveness study).

Authors:  Robert S Epstein; Thomas P Moyer; Ronald E Aubert; Dennis J O Kane; Fang Xia; Robert R Verbrugge; Brian F Gage; J Russell Teagarden
Journal:  J Am Coll Cardiol       Date:  2010-04-08       Impact factor: 24.094

2.  The microRNA spectrum in 12 body fluids.

Authors:  Jessica A Weber; David H Baxter; Shile Zhang; David Y Huang; Kuo How Huang; Ming Jen Lee; David J Galas; Kai Wang
Journal:  Clin Chem       Date:  2010-09-16       Impact factor: 8.327

Review 3.  Pervasive roles of microRNAs in cardiovascular biology.

Authors:  Eric M Small; Eric N Olson
Journal:  Nature       Date:  2011-01-20       Impact factor: 49.962

Review 4.  Interindividual variability in the response to oral antiplatelet drugs: a position paper of the Working Group on antiplatelet drugs resistance appointed by the Section of Cardiovascular Interventions of the Polish Cardiac Society, endorsed by the Working Group on Thrombosis of the European Society of Cardiology.

Authors:  Wiktor Kuliczkowski; Adam Witkowski; Lech Polonski; Cezary Watala; Krzysztof Filipiak; Andrzej Budaj; Jacek Golanski; Dariusz Sitkiewicz; Jerzy Pregowski; Jacek Gorski; Marian Zembala; Grzegorz Opolski; Kurt Huber; Harald Arnesen; Steen D Kristensen; Raffaele De Caterina
Journal:  Eur Heart J       Date:  2009-01-27       Impact factor: 29.983

5.  Most mammalian mRNAs are conserved targets of microRNAs.

Authors:  Robin C Friedman; Kyle Kai-How Farh; Christopher B Burge; David P Bartel
Journal:  Genome Res       Date:  2008-10-27       Impact factor: 9.043

6.  Circulating microRNAs as stable blood-based markers for cancer detection.

Authors:  Patrick S Mitchell; Rachael K Parkin; Evan M Kroh; Brian R Fritz; Stacia K Wyman; Era L Pogosova-Agadjanyan; Amelia Peterson; Jennifer Noteboom; Kathy C O'Briant; April Allen; Daniel W Lin; Nicole Urban; Charles W Drescher; Beatrice S Knudsen; Derek L Stirewalt; Robert Gentleman; Robert L Vessella; Peter S Nelson; Daniel B Martin; Muneesh Tewari
Journal:  Proc Natl Acad Sci U S A       Date:  2008-07-28       Impact factor: 11.205

7.  Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein.

Authors:  Paul M Ridker; Eleanor Danielson; Francisco A H Fonseca; Jacques Genest; Antonio M Gotto; John J P Kastelein; Wolfgang Koenig; Peter Libby; Alberto J Lorenzatti; Jean G MacFadyen; Børge G Nordestgaard; James Shepherd; James T Willerson; Robert J Glynn
Journal:  N Engl J Med       Date:  2008-11-09       Impact factor: 91.245

8.  MicroRNA-21 contributes to myocardial disease by stimulating MAP kinase signalling in fibroblasts.

Authors:  Thomas Thum; Carina Gross; Jan Fiedler; Thomas Fischer; Stephan Kissler; Markus Bussen; Paolo Galuppo; Steffen Just; Wolfgang Rottbauer; Stefan Frantz; Mirco Castoldi; Jürgen Soutschek; Victor Koteliansky; Andreas Rosenwald; M Albert Basson; Jonathan D Licht; John T R Pena; Sara H Rouhanifard; Martina U Muckenthaler; Thomas Tuschl; Gail R Martin; Johann Bauersachs; Stefan Engelhardt
Journal:  Nature       Date:  2008-11-30       Impact factor: 49.962

9.  Phenotypic predictors of response to simvastatin therapy among African-Americans and Caucasians: the Cholesterol and Pharmacogenetics (CAP) Study.

Authors:  Joel A Simon; Feng Lin; Stephen B Hulley; Patricia J Blanche; David Waters; Stephen Shiboski; Jerome I Rotter; Deborah A Nickerson; Huiying Yang; Mohammed Saad; Ronald M Krauss
Journal:  Am J Cardiol       Date:  2006-01-27       Impact factor: 2.778

Review 10.  The mammalian transcriptome and the function of non-coding DNA sequences.

Authors:  Svetlana A Shabalina; Nikolay A Spiridonov
Journal:  Genome Biol       Date:  2004-03-25       Impact factor: 13.583

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Authors:  Rocío Toro; Sara Blasco-Turrión; Francisco José Morales-Ponce; Pablo Gonzalez; Pablo Martínez-Camblor; Amador López-Granados; Ramon Brugada; Oscar Campuzano; Alexandra Pérez-Serra; Felix Rosa Longobardo; Alipio Mangas; Vicenta Llorente-Cortes; David de Gonzalo-Calvo
Journal:  J Mol Med (Berl)       Date:  2018-07-14       Impact factor: 4.599

Review 2.  Peripheral blood microRNAs and the COVID-19 patient: methodological considerations, technical challenges and practice points.

Authors:  Lucía Pinilla; Ivan D Benitez; Jessica González; Gerard Torres; Ferran Barbé; David de Gonzalo-Calvo
Journal:  RNA Biol       Date:  2021-02-15       Impact factor: 4.652

3.  Circulating miR-1254 predicts ventricular remodeling in patients with ST-Segment-Elevation Myocardial Infarction: A cardiovascular magnetic resonance study.

Authors:  David de Gonzalo-Calvo; Germán Cediel; Christian Bär; Julio Núñez; Elena Revuelta-Lopez; Josep Gavara; César Ríos-Navarro; Vicenta Llorente-Cortes; Vicente Bodí; Thomas Thum; Antoni Bayes-Genis
Journal:  Sci Rep       Date:  2018-10-11       Impact factor: 4.379

Review 4.  Circulating miRNAs as Diagnostic Biomarkers for Parkinson's Disease.

Authors:  Anna Elisa Roser; Lucas Caldi Gomes; Jonas Schünemann; Fabian Maass; Paul Lingor
Journal:  Front Neurosci       Date:  2018-09-05       Impact factor: 4.677

5.  Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury.

Authors:  Terezinha Souza; Panuwat Trairatphisan; Janet Piñero; Laura I Furlong; Julio Saez-Rodriguez; Jos Kleinjans; Danyel Jennen
Journal:  Front Genet       Date:  2018-11-20       Impact factor: 4.599

Review 6.  Significance of non-coding circular RNAs and micro RNAs in the pathogenesis of cardiovascular diseases.

Authors:  Guoan Zhao
Journal:  J Med Genet       Date:  2018-09-03       Impact factor: 6.318

Review 7.  Regulatory RNAs in Heart Failure.

Authors:  Clarissa Pedrosa Costa Gomes; Blanche Schroen; Gabriela M Kuster; Emma L Robinson; Kerrie Ford; Iain B Squire; Stephane Heymans; Fabio Martelli; Costanza Emanueli; Yvan Devaux
Journal:  Circulation       Date:  2020-01-27       Impact factor: 29.690

8.  Circulating microRNA profiles predict the severity of COVID-19 in hospitalized patients.

Authors:  David de Gonzalo-Calvo; Iván D Benítez; Lucía Pinilla; Amara Carratalá; Anna Moncusí-Moix; Clara Gort-Paniello; Marta Molinero; Jessica González; Gerard Torres; María Bernal; Silvia Pico; Raquel Almansa; Noelia Jorge; Alicia Ortega; Elena Bustamante-Munguira; José Manuel Gómez; Milagros González-Rivera; Dariela Micheloud; Pablo Ryan; Amalia Martinez; Luis Tamayo; César Aldecoa; Ricard Ferrer; Adrián Ceccato; Laia Fernández-Barat; Ana Motos; Jordi Riera; Rosario Menéndez; Dario Garcia-Gasulla; Oscar Peñuelas; Antoni Torres; Jesús F Bermejo-Martin; Ferran Barbé
Journal:  Transl Res       Date:  2021-05-25       Impact factor: 7.012

9.  Plasmatic and myocardial microRNA profiles in patients with Hypertrophic Cardiomyopathy.

Authors:  Maria Lombardi; Davide Lazzeroni; Giulia Benedetti; Gloria Bertoli; Dejan Lazarevic; Michela Riba; Francesco De Cobelli; Ornella Rimoldi; Giulia d'Amati; Iacopo Olivotto; Chiara Foglieni; Paolo Camici
Journal:  Clin Transl Med       Date:  2021-07

10.  Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids.

Authors:  David de Gonzalo-Calvo; Pablo Martínez-Camblor; Christian Bär; Kevin Duarte; Nicolas Girerd; Bengt Fellström; Roland E Schmieder; Alan G Jardine; Ziad A Massy; Hallvard Holdaas; Patrick Rossignol; Faiez Zannad; Thomas Thum
Journal:  Theranostics       Date:  2020-07-09       Impact factor: 11.556

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