Introduction
Heart failure (HF), as one of the most common causes of morbidity and mortality worldwide, is the ultimate result of most myocardial and vascular diseases including cardiomyopathies, myocardial infarction (MI), myocarditis, and functional heart disorders derived from hypertension, diabetes, infections or cardio-toxic drugs. Patients with HF suffer from symptoms of insufficient oxygen supply, dyspnea, arrhythmia, fatigue and weakness, edema and fluid retention, and reduced ability to exercise mainly due to impaired left ventricle (LV) myocardial dysfunction [
1,
2]. Currently, diagnosis of heart failure mostly relies on the physical examination and lab test, including the concentration of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in blood and ejection fraction (EF) value of the heart [
1,
2]. For the patients with HF, a > 30% NT-proBNP reduction after treatment predicts a good prognosis. And ≤ 30% reduction at discharge is always considered as a significant predictor of readmissions and mortality [
3]. However, changes of these diagnostic parameters occur only after functional or structural damage of the heart in patients. Identification of novel biomarkers for early prediction of HF is believed to be the most effective way to prevent HF development and/or slow down HF progression in the patients with cardiovascular disorders.
Small non-coding microRNAs (miRNAs), such as miR-1, miR-133, miR-208, and miR-301a have been reported to have important function in regulating heart development, heart-related diseases and LV remodeling [
4‐
6]. Circulating miRNAs in body fluids have been demonstrated to have potential as diagnostic and prognostic biomarkers in diverse diseases including human cancers and cardiovascular diseases [
7‐
11]. For example, a randomized Multicenter Italian Lung Detection (MILD) clinical trial study using 939 participants demonstrated a plasma-based miRNA signature classifier (MSC) has predictive, diagnostic, and prognostic value, and thus improving the efficacy of lung cancer screening [
12]. According to the information from the website of Clinicaltrials, a number of clinical studies have been registered for miRNAs as diagnostic or prognostic biomarkers in diverse diseases including coronary heart disease, diabetes, influenza, and multiple types of human cancer [
13].
A number of miRNAs have been reported to have potential and utility as biomarkers for predicting HF progression or evaluating the LV function [
8‐
11,
14‐
19]. A myocardium-enriched miRNA, miR-499 had an increased level in the blood circulation of patients with acute myocardial infarction (AMI) [
9,
16,
17]. Moreover, the increased level of miR-499 was present in plasma of patients earlier than other traditional AMI biomarkers like SMB, cTnI, cTnT, CK-MB, CK and LDH, suggesting its potential for early detection of AMI [
17]. In addition to miR-499, miR-1, miR-133a/b, and miR-30a were showed increase in the plasma of AMI patients, and in correlation with the cardiac damage degree [
8,
9,
18,
20]. Maciejak A. et al. [
8] identified circulating miR-30a-5p as a prognostic biomarker of the LV dysfunction after AMI by using a screening analysis and independent validation. miR-30a-5p showed significant increase in the plasma of patients with LV dysfunction and HF symptoms 6 months after AMI [
8]. Another analysis by Pergola V. et al. [
21] indicated the higher levels of circulating miR-30a and miR-21 in the patients with non-ischaemic HF, while lower levels of circulating miR-423 and miR-34a in the patients with ischaemic HF, suggesting a selective secretion of miRNAs by the damaged heart into the coronary circulation.
In the current study, we performed a miRNA screening analysis using HF inpatients’ plasma samples, and compared the paired samples between the time of check-in before any medical treatment and the time of check-out after partial or complete recovery. A subset of circulating miRNAs was identified to associate with medical treatment. miR-30a-5p and miR-654-5p were subsequently applied to plasma samples from HF patients and normal controls for the independent training and validation analyses. As a result, a novel 2-circulating miRNA model was developed, showing a high sensitivity of 98.75% and high specificity of 95.00% (AUC of 0.9978) for prognosis of HF. Moreover, changes of the two miRNAs were further verified in association with the therapeutic effect of HF patients before and after LVAD implantation.
Materials and methods
Phase definition
We applied three phases in this study. Discovery phase refers to the initial screening step of the study. Training phase was applied to confirm the results found in the discovery phase, and used to develop a diagnostic model. Validation phase was a larger independent cohort to further validate the diagnostic model developed in the training phase.
Patient cohorts
Patients were diagnosed as HF and admitted in hospital at Shanghai East Hospital. All the inpatients received echocardiography analysis and blood lab tests at Shanghai East Hospital. According to the “Guidelines for the diagnosis and treatment of acute and chronic heart failure” [
22], only those HF patients with EF ≤ 50% and NT-proBNP ≥ 450 pg/mL if less than 55 years old, or ≥ 900 pg/mL between 55 and 75 years old, or ≥ 1800 pg/mL if over 75 years old, and without other diseases were enrolled in the study.
In the discovery cohort (n = 40), only those patients were included when NT-proBNP decreased at least 30% after medical treatment with partial or complete LVEF recovery when leaving hospital, compared to the NT-proBNP value at check-in. According to the definition for identifying HF patients with a recovered LVEF by the JACC Scientific Expert Panel [
23], patients were considered as complete recovery of LVEF when EF > 50% or partial recovery when EF = 40–50%. The 30% NT-proBNP reduction was determined according to the Expert Consensus of Clinical Application of NT-proBNP [
24].
Medicines including β-receptor blocker, spironolactone, and sacubitril valsartan sodium tablets were given to those enrolled inpatients under the guidance of specialized doctors at Shanghai East Hospital. In the training cohort, 30 patients and 15 normal controls were enrolled. In the validation cohort, 50 patients and 25 normal controls were enrolled for the diagnostic model validation. Subjects in the training cohort and validation cohort were from the same hospital, but enrolled and organized by different physicians at different time period. Those HF patients with other diseases, such as diabetes and cancer, or having other medical treatment were exclusive from the enrollment of this study. A small RNA sequencing dataset from 27 patients with advanced heart failure without LVAD implantation, 10 patients with advanced heart failure with LVAD implantation for 3 months and 10 patients with advanced heart failure with LVAD implantation for 6 months [
15] were applied to further validate the changes of miR-30a-5p and miR-654-5p before and after medical therapy.
Clinical characteristics of the HF patient cohorts in the discovery, training and validation phases were listed in Additional file
1: Tables S1 and S2. The HF diagnosis was performed according to the World Health Organization standard diagnostic procedure. The HF stages were classified according to the symptoms of the patients following the guideline of New York Heart Association (NYHA) Functional Classification. The study was approved by the Institutional Review Board (IRB) of Shanghai East Hospital, Tongji University School of Medicine. All subjects were provided a written informed consent.
Determination of sample size
Following the principle of diagnostic studies, we calculated the sample size in training phase using the formula \(N(HF)=\frac{{Z\alpha }^{2}*Sn*\left(1-Sn\right)}{{\delta }^{2}}\) and \(N(control)=\frac{{Z\alpha }^{2}*Sp*\left(1-Sp\right)}{{\delta }^{2}}\) (Sn: sensitivity; Sp: specificity). As a result, a minimum size for the normal control group of 38 and minimum size for the HF group of 20 were obtained. In our study, 40 normal samples and 80 HF patient samples were applied to develop the diagnostic model.
Plasma collection and RNA extraction
Blood samples were collected into the EDTA-treated tubes from HF patients and normal controls at Shanghai East Hospital, followed by immediate centrifugation at the speed of 2000 rpm for 5 min at 4 °C. The supernatant plasma was stored in −80 ℃ freezer. Taking an aliquot of 200 µl for total RNA extraction by using 1 mL of Trizol reagent (Invitrogen, USA) following the standard protocol. Glycogen was used as an inert carrier to make RNA pellet visible. The quality of RNA was analyzed using Agilent Bioanalyzer 2100. All the procedures were approved by the Institutional Review Board (IRB) of Shanghai East Hospital, Tongji University School of Medicine.
miRNA QRT-PCR analysis
200 ng of total plasma RNA was applied to prepare the first strand cDNA of miRNAs by using the M&G miRNA Reverse Transcription kit (miRGenes, China) following the manufacturer’s instruction. The SYBR Green Master Mix (Applied Biosystem, USA) and QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystem, USA) were used for real-time PCR analysis. 5 s rRNA was used for normalization. Forward primer sequences for miR-30a-5p: 5′uguaaacauccucgacug 3′; miR-654-5p: 5′ugggccgcagaacaugu 3′; 5 s rRNA, 5′ agtacttggatgggagaccg 3′. All primer oligos were synthesized by GenScript (Nanjing, China).
Diagnostic model development
Binary logistic regression was applied for development of the miRNA diagnostic models by using IBM SPSS Statistics 26 software. The relationship between dependent Y scores (Control and HF as variables) and independent values (miR-30a-5p and miR-654-5p as variables) was analyzed. Based on relevant parameters in the binary logistic regression, three mathematical diagnostic models were developed, in which Y score 0.5 was set as cutoff. The samples were judged as HF if Y scores greater than 0.5, otherwise as normal.
miRNA target gene prediction and pathway analysis
Statistical analysis
Two-tailed p-values were calculated using paired samples t-tests in the discovery phase. Two-tailed t-test was used to analyzing the independent samples in the training and validation phases. The public datasets GSE53080 and GSE52601 were obtained from the NCBI-GEO database, in which the read counts were converted to transcripts per million (TPM) by using Python 3.7.4 software. For statistical analysis of the miRNA expression, log transformation of the values (2^-∆∆Ct) was applied in order to obtaining normal distribution of the miRNA expression levels [
25,
26]. Receiver operating characteristic (ROC) curves were drawn to calculate the area under the curve (AUC) and assess the diagnostic values using GraphPad Prism V8.0 software. p < 0.05 was considered as statistically significant difference.
Discussion
Cardiovascular diseases, as a major public health concern and the leading cause of death and disability globally, are responsible for around 17.18 million deaths every year, representing over 30% of all global deaths according to the health report of World Health Organization in 2019. Most cardiovascular diseases eventually progress to HF, which is associated with a 5-year survival as low as 25% [
28]. To make matters worse, the incidence is continuing to increase along with the aging of the general population all over the world. Early diagnosis of cardiovascular diseases has been considered as one of the most promising attempts for reducing the risk and mortality. Identification of new diagnostic biomarkers and development of effective diagnostic models are often the main focus of concern, with potential to be adopted for clinical identification of individuals at high risk for the development of HF.
The present study was started with a comparative analysis of paired plasma samples from same inpatient at the time of check-in before medical treatment and the time of check-out after getting better, which minimized the interference from individual variation of subjects. Six circulating miRNAs were identified in association with medical treatment in HF patients. Among them, the abundances of miR-30a-5p, miR-100, miR-499b, miR-320a and miR-433-3p in plasma showed positive associations, while miR-654-5p showed a negative association with the severity of the heart illness in patients.
Consistent with literature, 5 of the 6 miRNAs we identified have been previously reported to have aberrant expression levels in the circulation and/or heart tissues of patients with cardiovascular diseases. For example, the level of miR-499 increased in the circulation of patients with AMI [
16,
17]; circulating miR-30a-5p showed a higher level in the patients with non-ischaemic HF [
21]; the expression of miR-100 increased in the tissue samples from both idiopathic and ischemic cardiomyopathies hearts [
29]; the levels of circulating miR-320 and miR-433-3p increased in the patients with coronary artery disease [
30,
31] and critical coronary stenosis [
32], respectively. Consistence with literature is indicative of the reliability of our analysis. Nevertheless, we are the first to identify circulating miR-654-5p as a potential biomarker in the patients with HF. Another novelty is that the current study demonstrated an association of the circulating miRNA levels with the therapeutic effect in HF patients, which provides a potential biomarker for determining the prognosis.
We further confirmed miR-30a-5p and miR-654-5p having a high potential to serve as biomarkers for diagnosis of HF. Accordingly a diagnostic model containing miR-30a-5p and miR-654-5p was developed. The high sensitivity and high specificity of the model were validated through two independent cohorts and an external public dataset, in which the two circulating miRNAs showed consistent correlations with HF. miR-30a-5p has been reported to be highly expressed in the damaged heart [
33] and patients with HF, left ventricular dysfunction, and left ventricular hypertrophy [
8,
20,
34]. Silencing of miR-30a-5p promoted recovery of cardiac injury, and protected cardiomyocytes from the impact of hypoxia/reoxygenation [
35]. Studies of miR-654-5p in regulating cancer has been reported [
36]. However, the function of miR-654-5p in the heart remains unclear.
There is a limitation of the two-miRNA model we identified that it was not tested against patients with other disease. As the next step, further validation by multiple-center and large-scale investigation using larger patient cohorts will be of great help to strengthen the value of the two-miRNA model for clinical application. In addition, the tissue origin of circulating miRNAs is still an open question in the field. In HF, we do suppose the change of the levels of circulating miR-30a-5p and miR-654-5p is most likely contributed by the heart tissue and/or the responding cells.
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