Abstract
Heart failure (HF), as the leading cause of death, is continuing to
increase along with the aging of the general population all over the
world. Identification of diagnostic biomarkers for early detection of
HF is considered as the most effective way to reduce the risk and
mortality. Herein, we collected plasma samples from HF patients
(n = 40) before and after medical therapy to determine the change of
circulating miRNAs through a quantitative real-time PCR (QRT-PCR)-based
miRNA screening analysis. miR-30a-5p and miR-654-5p were identified as
the most significantly changed miRNAs in the plasma of patients upon
treatment. In consistence, miR-30a-5p showed upregulation and
miR-654-5p showed downregulation in the circulation of 30 HF patients,
compared to 15 normal controls in the training phase, from which a
two-circulating miRNA model was developed for HF diagnosis. Next, we
performed the model validation using an independent cohort including 50
HF patients and 30 controls. As high as 98.75% of sensitivity and
95.00% of specificity were achieved. A comparison between the miRNA
model and NT-pro BNP in diagnostic accuracy of HF indicated an upward
trend of the miRNA model. Moreover, change of the two miRNAs was
further verified in association with the therapeutic effect of HF
patients, in which miR-30a-5p showed decrease while miR-654-5p showed
increase in the plasma of patients after LVAD implantation. In
conclusion, the current study not only identified circulating
miR-654-5p for the first time as a novel biomarker of HF, but also
developed a novel 2-circulating miRNA model with promising potentials
for diagnosis and prognosis of HF patients, and in association with
therapeutic effects as well.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12967-022-03465-w.
Keywords: Heart failure, Diagnosis, Circulating miRNA, Biomarker
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 [[51]1, [52]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 [[53]1, [54]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 [[55]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
[[56]4–[57]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
[[58]7–[59]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 [[60]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 [[61]13].
A number of miRNAs have been reported to have potential and utility as
biomarkers for predicting HF progression or evaluating the LV function
[[62]8–[63]11, [64]14–[65]19]. A myocardium-enriched miRNA, miR-499 had
an increased level in the blood circulation of patients with acute
myocardial infarction (AMI) [[66]9, [67]16, [68]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 [[69]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 [[70]8, [71]9, [72]18, [73]20]. Maciejak A. et
al. [[74]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 [[75]8]. Another analysis by Pergola V. et al. [[76]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”
[[77]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 [[78]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 [[79]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 [[80]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 [81]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
[MATH: N(HF)=Z<
/mi>α2∗S
n∗1-Snδ2 :MATH]
and
[MATH: N(control)=Z<
/mi>α2∗S
p∗1-Spδ2 :MATH]
(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
The ENCORI tool ([82]https://starbase.sysu.edu.cn/index.php) was used
to predict the target genes of miR-30a-5p and miR-654-5p. Pathway
analyses were performed using web-based gene set analysis toolkit
(WebGestalt, [83]http://www.webgestalt.org/).
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 [84]GSE53080 and [85]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 [[86]25, [87]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.
Results
Characteristics of subjects
A total of 160 subjects, including 120 HF patients and age-matched 40
normal controls were included in the current study to determine the
circulating miRNA signature for diagnosis of HF patients. All the
characteristics of the patients in the discovery phase, training phase
and validation phase were shown in Additional file [88]1: Table S1 and
S2. The flow chart of the study design was shown in Fig. [89]1.
Fig. 1.
[90]Fig. 1
[91]Open in a new tab
Overview of the study design
Identification of the medical therapy-associated circulating miRNAs in HF
patients
In order to identify the circulating miRNAs associated with HF
progression and medical treatment, 40 HF inpatients were selected for
plasma collection and circulating miRNAs screening analysis. All of
these patients had NT-proBNP decreased at least 30% after therapy when
leaving hospital, compared to the values at check-in. Comparative
analyses of the circulating miRNA levels before and after medical
treatment were performed for all the 40 patients. A QRT-PCR-based
home-made miRNA panel containing 89 cardiovascular-related human miRNAs
(Additional file [92]1: Table S3) were applied. As a result, a subset
of six miRNAs were found to have significant change (p < 0.05) in
abundance in the plasmas (Fig. [93]2A, before treatment vs after
treatment). Among them, miR-30a-5p, miR-100, miR-499b, miR-320a and
miR-433, showed significant downregulation, while miR-654-5p showed
upregulation in plasma of those patients after therapy. After applying
cutoffs of absolute fold change (|FC|) > 1.5 and p-value < 0.05,
miR-30a-5p and miR-654-5p were screened out as the most significantly
changed candidate miRNAs of our interest (Fig. [94]2A). The expression
patterns of the six circulating miRNAs in the paired plasma samples
indicated higher levels of miR-30a-5p, -100, -499b, -320a and -433 and
lower level of circulating miR-654-5p in the HF patients before
therapy, while changed to an opposite direction after medical therapy
(Additional file [95]1: Figure S1, Fig. [96]2B and C). The expression
patterns and change trends of the rest of the circulating miRNAs were
shown in Additional file [97]1: Figure S2.
Fig. 2.
Fig. 2
[98]Open in a new tab
Identification of the medical therapy-associated circulating miRNAs in
heart failure (HF) patients. A QRT-PCR based screening of circulating
miRNAs in the plasma samples of HF patients (n = 40) before and after
medical treatment. Comparisons were made by before treatment vs after
treatment. Six miRNAs were identified to be significantly associated
with the therapeutic effect, including miR-654-5p with upregulation and
miR-30a-5p, 449b, 320a, 433 and 100 with downregulation after
treatment. By applying cutoffs with fold change (FC) > 1.5 and
p < 0.05, miR-30a-5p and miR-654-5p were screened out as the most
significantly changed miRNAs. B, C Expression patterns of miR-30a-5p
(B) and miR-654-5p (C) in each inpatient in the discovery cohort with
paired plasma samples collected at times of check-in and check-out.
Two-tailed p-values were calculated using paired samples t-tests
Model development with the circulating miR-30a-5p/miR-654-5p signature
In order to determine the diagnostic potential of circulating
miR-30a-5p and miR-654-5p in HF, the training phase was designed to
confirm the expression of circulating miR-30a-5p and miR-654-5p in the
plasma samples from 30 HF patients and 15 normal controls. Consistent
with the results in the discovery phase, circulating miR-30a-5p showed
a significantly higher level while circulating miR-654-5p showed a
significantly lower level in HF patients, compared with normal controls
(Fig. [99]3A, B).
Fig. 3.
[100]Fig. 3
[101]Open in a new tab
Model development with the circulating miR-30a-5p/miR-654-5p signature
in the training cohort. A, B Quantitative analysis of circulating
miR-30a-5p (A) and miR-654-5p (B) in the 30 HF patients and 15 normal
controls (Ctrl) in the training phase. C Formulas of the miRNA
diagnostic models developed using binary logistic regression method.
Three models covering miR-30a-5p (model 1) or miR-654-5p (model 1) and
both miRNAs (model 3) were developed and displayed, respectively. D–F
Receiver operating characteristic (ROC) curves of model 1 (D), model 2
(E) and model 3 (F). The values of AUC, 95% CI and p were indicated in
the graphs
To assess the diagnostic values of the two circulating miRNAs, the
binary logistic regression method was applied to develop the diagnostic
models of the two miRNAs under individual or combination conditions.
Three models were derived with the corresponding calculating formulas
shown in Fig. [102]3C, in which Y score > 0.5 was considered as HF, and
Y score < 0.5 as normal. In order to assess the specificity and
sensitivity of the three models in diagnosis of HF, Receiver Operating
Characteristic (ROC) curves were drawn as shown in Fig. [103]3D–F, in
which the Area Under Curve (AUC) was 0.9089, the diagnostic sensitivity
was 83.33% and the diagnostic specificity was 80.00% for model 1
(Fig. [104]3C, D), AUC 0.9867, sensitivity 96.67% and specificity
86.67% for model 2 (Fig. [105]3C, E), and AUC 0.9978, sensitivity
96.67% and specificity 93.33% for model 3 (Fig. [106]3C, F). The data
suggested more contribution of miR-654-5p to HF diagnosis, and the best
diagnostic accuracy of model 3 covering the both miRNAs. These results
indicated that even individual miR-30a-5p or miR-654-5p in plasma can
be effective in diagnosis of HF, a combination model showed a higher
sensitivity and better specificity.
Independent validation of the circulating miR-30a-5p/miR-654-5p model
In order to further validate the diagnostic values of circulating
miR-30a-5p, miR-654-5p and the models developed in the training phase
using 30 HF patients and 15 normal controls in Fig. [107]3, additional
50 HF patients and 25 normal controls were applied in the validation
phase for the miRNA analysis and model validation in Fig. [108]4.
Similar to the results in the training phase, here we validated the
increased level of circulating miR-30a-5p (Fig. [109]4A) and the
decreased level of circulating miR-654-5p (Fig. [110]4B) in HF
patients, compared with normal controls. The ROC curves also validated
the highest sensitivity and the best specificity of model 3 for
diagnosis of HF (Fig. [111]4C–F).
Fig. 4.
[112]Fig. 4
[113]Open in a new tab
Independent validation of the miRNA models in the validation cohort. A,
B Quantitative analysis of circulating miR-30a-5p (A) and miR-654-5p
(B) in additional 50 HF patients and 25 controls (Ctrl) in the
validation phase. C Diagnostic accuracy, AUC and 95% CI of the three
models tested in the subjects of the validation cohort. D–F Receiver
operating characteristic (ROC) curves of the three models in the
validation cohort
Next, we combined all subjects in the training phase and validation
phase together, including 80 patents and 40 normal controls in total,
and applied to confirm the diagnostic value of the models. The
abundance levels of circulating miR-30a-5p and miR-654-5p in the 80
patents and 40 controls were shown in Fig. [114]5A and B. Among the
three models, model 3 showed the highest accuracy of 97.50% with AUC
0.9841 as shown in Additional file [115]1: Figure S3 and S4. When
applied for prognosis with the 120 subjects, model 3 gave 98.75% of
true positive rate for HF (Fig. [116]5C, 5D) and 95.00% of true
negative rate for normal controls (Fig. [117]5E). In addition, we
compared the prognostic accuracy between the miRNA model we developed
and the traditional approach using NT-proBNP level in serum. As shown
in Fig. [118]5F, NT-proBNP-based HF prognosis (following the standard
in literature [[119]27]) showed 97.5% of prognostic accuracy
(Fig. [120]5F), a little big lower than our miRNA model (Fig. [121]5E).
Among the 80 clinically diagnosed HF patients, 77 were diagnosed as HF
positive by both the miRNA-based model and the NT-proBNP-based approach
(Fig. [122]5G).
Fig. 5.
[123]Fig. 5
[124]Open in a new tab
Further validation of the miRNA diagnostic model by combining the two
independent clinical cohorts. A, B Expression levels of circulating
miR-30a-5p (A) and miR-654-5p (B) in the 80 HF patients and 40 controls
(Ctrl) combining the training and validation cohorts. C Y scores
distribution plot of model 3 in the all subjects combining the training
and validation cohorts. Y scores greater than 0.5 were judged as HF,
otherwise as normal. D Pie graph showing the true-positive rate
(98.75%) and false negative rate (1.25%) of model 3 in diagnosis of the
80 HF patients. E Pie graph showing the true-negative rate (95.00%) and
false positive rate (5.00%) of model 3 tested in the 40 normal controls
(Ctrl). F Pie graph showing the true-positive rate (97.50%) and false
negative rate (2.50%) of the 80 HF patients by NT-proBNP-based
diagnosis. G Venn graph showing overlaps between the clinically
diagnosed 80 HF patients, the miRNA model-based model diagnosis and the
NT-proBNP-based diagnosis
The correlations between miR-30a-5p, miR-654-5p and NT-proBNP were
further analyzed in the 40 HF patients enrolled in the discovery phase.
Comparisons between the levels of miR-30a-5p and miR-654-5p vs the
abundance of NT-proBNP in the plasma samples of the HF patients before
and after treatment were performed. As shown in Additional file [125]1:
Figure S3, a significantly negative correlation between miR-654-5p and
NT-proBNP in plasma of HF patients was observed, which is consistent
with the results and the model we developed. As expected, miR-30a-5p
showed a positive correlation with NT-proBNP. However, analysis on a
larger cohort of HF samples is still required to evaluate its
significance.
The correlation between the differential miRNA expression and the
NT-proBNP reduction was further analyzed by applying a comparison of
the miRNA expression change between 5 patients with ~ 80% NT-proBNP
reduction and 5 patients with ~ 40% NT-proBNP reduction upon medical
therapy. As shown in the revised Additional file [126]1: Figure S4,
more changes of both miR-654-5p and miR-30a-5p levels were observed in
the patients with more NT-proBNP reduction, further suggesting the
regulation of the two miRNA levels by medical therapy in HF patients.
The correlation between the miR-30a-5p/miR-654-5p signature and
pathologic characteristics in the patients was also analyzed. The 80
patients were classified into two groups according to the NYHA
Functional Classification, by which 25 patients with NYHA class I and
II were grouped to HF(I + II), and 55 patients with NYHA class III and
IV were grouped to HF(III + IV). As shown in Fig. [127]6A and B, the
higher level of miR-30a-5p and lower lever of miR-654-5p were indicated
in the patients, but did not show significant difference between the
two classification groups. Additional analysis between the miRNA
signature and cardiomyopathy types in the patients was performed.
Cardiomyopathy, as one of the main reasons causing heart failure and
even sudden death, is classified to ischemic cardiomyopathy (CM) and
dilated non-ischaemic cardiomyopathy (DCM). As seen in Fig. [128]6C and
D, circulating miR-30a-5p showed increase, while circulating miR-654-5p
showed decrease in the patients without correlation with the
cardiomyopathy types.
Fig. 6.
[129]Fig. 6
[130]Open in a new tab
Correlation analysis of the miRNA signature with HF classification and
subtypes. A, B Expression levels of circulating miR-30a-5p (A) and
miR-654-5p (B) in the 40 controls (Ctrl), 25 patients with NYHA class I
and II and 55 patients with NYHA class III and IV. C, D Expression
levels of circulating miR-30a-5p (C) and miR-654-5p (D) in the 40
controls (Ctrl), 21 patients with Ischemic cardiomyopathy (ICM), 42
patients with dilated cardiomyopathy (DCM) and 17 patients with other
cardiovascular diseases. p < 0.05 was considered as statistically
significance. ns means non significant
Cardiovascular disease-related target gene analysis of miR-30a-5p and
miR-654-5p
In order to reveal the function of the two miRNAs, a total of 2871 and
3371 genes were predicted as potential targets of miR-30a-5p and
miR-654-5p, respectively, by using ENCORI tool. In addition, 902
differentally expressed genes (DEG) between HF patients and healthy
controls were obtained by analyzing a public dataset [131]GSE52601.
Among the potential target genes, DEG in HF and cardiovascular
disease-related genes in KEGG, 11 genes and 9 genes were overlapped for
miR-30a-5p and miR-654-5p, respectively (Additional file [132]1: Table
S4 and S5). KEGG pathway enrichment analysis indicated that miR-30a-5p
may involve in pathways regulating apoptosis, p53 signaling, viral
myocarditis, et al., and miR-654-5p may regulate pathways of dilated
cardiomyopathy (DCM), p53 signaling, atherosclerosis, adrenergic
signaling in cardiomyocytes, et al. (Fig. [133]7).
Fig. 7.
[134]Fig. 7
[135]Open in a new tab
Additional validation of the miRNA signature in association with the
therapeutic effect. A, B Expression levels of circulating miR-30a-5p
(A) and miR-654-5p (B) in 27 HF patients without LVAD implantation
(pre_ LVAD), 10 HF patients with LVAD implantation for 3 months (3M_
LVAD) and 10 HF patients with LVAD implantation for 6 months. The miRNA
expression data was obtained from dataset [136]GSE53080. p < 0.05 was
considered as statistically significance. ns means non significant
Additional validation of the circulating miR-30a-5p/miR-654-5p signature in
association with the therapeutic effect
In order to further verify the miRNA signature we identified, an
external dataset was applied, which contains circulating miRNA profiles
in the patients with advanced heart failure with or without medical
treatment with left ventricular assist devices (LVAD) implantation.
Compared to 27 patients without LVAD implantation, significant decrease
of miR-30a-5p (Fig. [137]8A) and increase of miR-654-5p (Fig. [138]8B)
were found in 10 patients receiving LVAD implantation for 3 months and
another 10 patients receiving LVAD implantation for 6 months, which was
associated with the improved myocardial function. These data further
demonstrated the reliability and utility of the circulating miR-30a-5p
and miR-654-5p as biomarkers in diagnosis of HF, and in association
with the therapeutic effects as well.
Fig. 8.
[139]Fig. 8
[140]Open in a new tab
Analysis of the cardiovascular disease-related target genes of
miR-30a-5p and miR-654-5p. A 11 target genes of miR-30a-5p were
overlapped among 2871 predicted target genes, 594 cardiovascular
disease genes and 902 DEGs in HF. B 9 target genes of miR-654-5p were
overlapped among 3371 target genes, 594 cardiovascular disease genes
and 902 DEGs in HF. KEGG pathway enrichment analysis was applied by
using WebGestalt
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%
[[141]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 [[142]16, [143]17]; circulating
miR-30a-5p showed a higher level in the patients with non-ischaemic HF
[[144]21]; the expression of miR-100 increased in the tissue samples
from both idiopathic and ischemic cardiomyopathies hearts [[145]29];
the levels of circulating miR-320 and miR-433-3p increased in the
patients with coronary artery disease [[146]30, [147]31] and critical
coronary stenosis [[148]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 [[149]33] and patients with HF, left ventricular dysfunction, and
left ventricular hypertrophy [[150]8, [151]20, [152]34]. Silencing of
miR-30a-5p promoted recovery of cardiac injury, and protected
cardiomyocytes from the impact of hypoxia/reoxygenation [[153]35].
Studies of miR-654-5p in regulating cancer has been reported [[154]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.
Supplementary Information
[155]12967_2022_3465_MOESM1_ESM.pdf^ (1.2MB, pdf)
Additional file 1: Table S1. Characteristics of HF patients in the
discovery phase (n=40). Table S2. Characteristics of HF patients in the
training phase (n=30) and validation phase (n=50). Table S3. Home made
circulating miRNA panel which was used for the miRNA screening analysis
in the discovery phase. Table S4. The features of 11 overlapped target
genes of miR-30a-5p in Heart Failure (ICM or DCM). Table S5. The
features of 9 overlapped target genes of miR-654-5p between Heart
Failure (ICM or DCM). Figure S1. Expression patterns of circulating
miR-100, miR-320a, miR-433 and miR-499b-3p in each patient in the
discovery phase with paired plasma samples collected at times of
check-in and check-out. The p-values showed significant change of the
four miRNAs (p < 0.05). Figure S2. Expression patterns and change
trends of the rest of 83 circulating miRNAs in all patients in the
discovery phase. The p-values of these miRNAs did not show significant
change. Figure S3. Correlation analysis between miR-30a-5p, miR-654-5p
and NT-proBNP in the 40 HF patients enrolled in the discovery phase
before and after treatment. Figure S4. Correlation analysis between the
differential miRNA expression change and the NT-proBNP reduction by
comparing 5 patients with ~80% NT-proBNP reduction and 5 patients with
~40% NT-proBNP reduction upon medical therapy (n=5, *p < 0.05).
Author contributions
ZY, ZL and HS designed the research and wrote the paper. LQ, JL, and YG
performed experiments. PY, XG, YZ and HF collected the patient samples;
YD, SY and LF did lab tests; QZ did data analysis. All authors read and
approved the final manuscript.
Funding
This work was supported by Grant 18411965900 from the Science and
Technology Commission of Shanghai Municipality, Grant 81800243 from the
National Natural Science Foundation of China.
Availability of data and materials
All data and materials related in this research are available for
sharing.
Declarations
Ethics approval and consent to participate
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.
Consent for publication
I give my consent for the article to publish in Journal of
translational medicine, section of disease biomarkers.
Competing interests
The authors declared no competing interest exists.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Lu Qian, Qian Zhao and Ping Yu contributed equally to this work
Contributor Information
Zhongmin Liu, Email: liu.zhongmin@tongji.edu.cn.
Hongzhuan Sheng, Email: yjshz@ntu.edu.cn.
Zuoren Yu, Email: zuoren.yu@tongji.edu.cn.
References