- Open Access
Identification of a circulating microRNAs biomarker panel for non-invasive diagnosis of coronary artery disease: case–control study
BMC Cardiovascular Disorders volume 22, Article number: 286 (2022)
Circulating microRNAs (miRNAs) are considered a hot spot of research that can be employed for monitoring and/or diagnostic purposes in coronary artery disease (CAD). Since different disease features might be reflected on altered profiles or plasma miRNAs concentrations, a combination of miRNAs can provide more reliable non-invasive biomarkers for CAD.
Subjects and methods
We investigated a panel of 14-miRNAs selected using bioinformatics databases and current literature searching for miRNAs involved in CAD using quantitative real-time PCR technique in 73 CAD patients compared to 73 controls followed by function and pathway enrichment analysis for the 14-miRNAs.
Our results revealed three out of the 14 circulating miRNAs understudy; miRNAs miR133a, miR155 and miR208a were downregulated. While 11 miRNAs were up-regulated in a descending order from highest fold change to lowest: miR-182, miR-145, miR-21, miR-126, miR-200b, miR-146A, miR-205, miR-135b, miR-196b, miR-140b and, miR-223. The ROC curve analysis indicated that miR-145, miR-182, miR-133a and, miR-205 were excellent biomarkers with the highest AUCs as biomarkers in CAD. All miRNAs under study except miR-208 revealed a statistically significant relation with dyslipidemia. MiR-126 and miR-155 showed significance with BMI grade, while only miR-133a showed significance with the obese patients in general. MiR-135b and miR-140b showed a significant correlation with the Wall Motion Severity Index. Pathway enrichment analysis for the miRNAS understudy revealed pathways relevant to the fatty acid biosynthesis, ECM-receptor interaction, proteoglycans in cancer, and adherens junction.
The results of this study identified a differentially expressed circulating miRNAs signature that can discriminate CAD patients from normal subjects. These results provide new insights into the significant role of miRNAs expression associated with CAD pathogenesis.
Cardiovascular diseases (CVDs) are one of the top causes of patients’ mortality all over the world [1, 2]. In Egypt, CVDs have also been the leading cause of premature death. Since 1990, they accounted for 46.2% of the overall mortality in Egypt in 2017 . Coronary artery disease (CAD) is the most prevalent among CVDs, and its incidence is high apart of the socioeconomic status of the patient . These figures highlight the urge to discover new CVD biomarkers for the prevention and treatment of those diseases.
Currently, the common diagnosis of CAD is based on coronary angiography, an invasive technique visualizing the positional structure of the coronary artery and it is considered the gold standard for CAD diagnosis . Owing to the known complications of invasive techniques, the emergence of non-invasive and non-imaging techniques offers excellent opportunities .
MicroRNAs (miRNAs) are non-coding, single-stranded RNAs with 20–22 nucleotides in length . Their primary function is to block mRNA translation to protein via binding to complementary sequences on messenger RNA (mRNA). About 1,900 unique human miRNAs have been identified till now, and most of them inhibit and target gene expression for hundreds of genes . In addition, it is estimated that miRNAs regulate about 60% of human protein-coding genes and each miRNA targets multiple mRNAs .
In the dilemma of discovering non-invasive biomarkers in CVDSs, major scientific endeavors have been turned to the identification of circulating miRNAs as diagnostic, prognostic, and therapeutic biomarkers in many diseases, including CAD .
Although several biological molecules, including peptides, proteins, cytokines, and different metabolites, are currently being used as biomarkers for CVDs , circulating miRNAs possess many attractive features of biomarkers owing to their stability as they are not degraded by endogenous RNases in the circulation [12, 13].
Several studies have described the role of circulating miRNAs as early diagnostics biomarkers in CAD. At the same time, others demonstrated their prognostic and therapeutic potential interventions in CAD . So, circulating miRNAs are considered now a hot spot of research that can be employed for monitoring and/or diagnostic purposes of CVDs. Moreover, since different disease features might be reflected on altered profiles or plasma/serum miRNAs concentrations, a combination of miRNAs will provide more reliable biomarkers .
In this vicinity, our study investigated the differential expression of a panel of 14-miRNAs selected using bioinformatics databases and current literature searching for miRNAs suspected to be involved in CAD pathogenesis and have putative binding sites for the most affected genes in CAD.
Subjects and methods
This study was a case–control study with 146 participants classified into two groups. The first group included 73 patients presenting with symptoms or findings suggesting CAD by clinical examination and diagnostic tools (Echo and ECG) recruited from the cardiology clinic at the Suez Canal University Hospital (SCUH) from June 2020 till June 2021. All details on study subjects are available in Additional files 1, 2.
Selection of miRNAs under study using bioinformatics tools
The miRNAs under study were selected using bioinformatics online tools as HMDD (http://www.cuilab.cn/) , and miR2Disease (http://www.mir2disease.org/) . Also, we searched available literature for the most common miRNAs involved in CAD pathogenesis. All details related to miRNAs selected based on literature are available in Additional file 2.
Blood samples collection
Three ml of fresh venous blood was collected from all study participants in vacutainer tubes containing ethylene diamine tetraacetic acid (EDTA) anticoagulant. The samples were centrifuged to separate plasma; 100 μl plasma was preserved in 500 μl Qiazole reagent. The plasma samples were stored at − 80℃ till further analysis.
MicroRNA extraction and quality analysis
Total RNA was isolated using Qiagen miRNeasy Mini kit (cat no 217004, QIAGEN, Hilden, Germany) following the modified protocol supplied by the manufacturer. RNA concentration and purity were determined using NanoDrop 2000 1C spectrophotometer (NanoDrop Tech., Inc. Wilmington, DE, USA).
Circulating miRNAs relative expression analysis using quantitative real-time PCR assay
The expression profile of 14 circulating miRNAs involved in CAD pathogenesis was assessed in the plasma of all study participants using Real Time-Polymerase Chain Reaction (RT-PCR). This was done via a two-step approach as follows; (a) reverse transcription (RT), and (b) quantitative Real-Time PCR, where the premix of cDNA was used as a template for relative quantification of the 14 human miRNAs under study, which are miR-21-3p, miR-126-5p, miR-145-5p, miR-155-3p, miR-208a-5p, miR-140-3p, miR-182-5p, miR-146a-5p, miR-223-5p, miR-196b-5p, miR-200b-3p, miR-205-5p, miR-133a-5p, and miR-135b-5p. All details related to RT and Real-Time PCR conditions are available in Additional file 2.
Assessment of circulating miRNAs predictive significance as biomarkers
The contribution to the predictive capacity of the significant miRNAs was analyzed using Receiver Operating Characteristic (ROC) curves to evaluate the diagnostic value of the used miRNAs as biomarkers for CAD pathogenesis. A p-value of < 0.05 was considered statistically significant.
Function and pathway enrichment analysis
The functional enrichment analysis was conducted using the software Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) , where gene ontology (GO) consisting of biological processes, cellular components, and molecular functions terms was searched for via Pathway analysis on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  for determining the pathways affected with differential miRNA expression and their target genes. More details on function and pathway enrichment analysis are available in Additional file 2.
MiRNA‐mRNA regulatory network construction
The targets of the homogenously statistically significant DEmiRNAs were predicted using miRTargetLink 2.0 (Version 2.0, https://ccb-compute.cs.uni-saarland.de/) . More details on miRNA-mRNA regulatory network construction are available in Additional file 2.
Data were analyzed using R software version 3.3.2, GraphPad prism 7, SPSS software version 23.0, and PC-ORD ver. 5.0. We used the G*Power 184.108.40.206. with the specified study design (gene expression), alpha error = 0.05, an effect size = 0.74, and a total sample size of 146 was calculated that can give 80% power of the study http://www.gpower.hhu.de/A . Fold change of the miRNAs was estimated using the LIVAC method (= 2−ΔΔCq) . More details on statistical analysis are available in Additional file 2.
Baseline characteristics and CAD risk factors among the study participants
Baseline data from all study participants in both control and CAD groups were presented in table (1). The age of participants showed an average of 38.34 ± 11.90 and 54.93 ± 9.56 years in controls and study groups, respectively. The CAD group showed significantly higher age (p < 0.001***) than the control group. Subjects aged over 55 years were nearly five times prone to develop CAD (OR = 4.9, 95% CI: 2.2–10.8, p = 0.001) compared to subjects aged 18 to 55 years. The male gender was more represented in control and CAD groups with 52 (71.2%) and 55 (75.3%) patients in the control and the CAD group, respectively, with a non-statistical difference (p > 0.05) among the two groups. About CAD risk factors, smokers were significantly (p < 0.001***) higher in the CAD group with a total of 42 (57.5%) smokers compared to the control group, which included 21 (28.8%) smokers. Smokers were nearly three times more prone to develop CAD (OR = 3.4, 95% CI: 1.7–6.7, p = 0.001) than non-smokers. Family history was found for 45 (61.6%) patients compared to 25 (34.2%) subjects in the control group, with a highly significant difference between the two groups. CAD patients with positive family history were nearly three times more prone to develop CAD (OR = 3.1, 95%CI: 1.6–6.1, p = 0.001) than patients with negative family history. Concerning dyslipidemia, patients were significantly higher in CAD group 59 (80.8%) compared to the control group 3 (4.1%). Dyslipidemia was shown to be a significant risk factor in our CAD patients with nearly 98 times prone to develop CAD (OR = 98.3, 95%CI: 27–358.7, p = 0.001) compared to subjects with standard lipid profile. The average (± SD) BMI of the CAD group (30.16 ± 5.68) was significantly higher (p = 0.011) than the control group (27.69 ± 3.99), as shown in Table 1. CAD patients with obesity were nearly two times more to develop CAD (OR = 2.4, 95%CI: 1.2–4.8, p = 0.011) than non-obese patients.
Comorbidities, clinical and cardiovascular findings among CAD patients
Table 2 shows the comorbidities clinical and cardiovascular findings among the CAD patients under study. Concerning comorbidities, there was no statistical significance between CAD and either diabetes, hypertension, or ischemic heart disease (IHD) among our study population. Clinical examination revealed an average (± SD) for Body surface area (BSA) of 1.84 ± 0.18, Systole 127.33 ± 17.28, diastole of 81.37 ± 16.14, Left Ventricular Ejection Fraction (LVEF) of 46.58 ± 13.25. Grades of LVEF represented by grades from normal to severe were 22 (30.14%), 15 (20.55%), 24 (32.88%), and 12 (16.44%), respectively. Grade 3 was the highest with a statistically significant difference, as revealed by the Chi-squared test. The average (± SD) WMSI was recorded as 1.49 ± 0.44. The diastolic grade represented from normal to severe were represented by 4 (5.48%), 42 (57.53%), 22 (30.14%), and 5 (6.85%), with a highly significant difference between grades.
Circulating miRNAs relative expression analysis
The differential expression patterns of the 14 miRNAs under study (miR-21, miR-126, miR-133a, miR-135b, miR-140, MiR-145, miR-146a, miR-155, miR-182, miR-196b, miR-200b, miR-205, miR-208a, miR-223) were investigated by qRT‐PCR and shown in (Fig. 1). Out of the 14 circulating miRNAs; miRNAs miR133a, miR155 and miR208a were down-regulated in CAD patients compared to the control group and recorded a median (IQR) of 3.89 (− 6.85 to − 0.84), − 1.89(− 4.28 to − 0.62) and 0.12(− 3.96–3.47) respectively (Fig. 2).
However, when sorting the relative expression patterns in the rest of the up-regulated 11 miRNAs in a descending order from highest fold change to lowest, the following order was obtained: miR-182 6.12 (4.12–7.12), miR-145 5.12(3.12–6.76); miR-21 4.68(1.58–7.59); miR-126 3.75(1.35–7.58), miR-200b 3.71(0.80–6.62), miR-146A 3.62(1.15–7.62), miR-205 3.58(1.81–5.68), miR-135b 2.62(0.62–5.18); miR-196b 2.43(0.18–4.44), miR-140b 2.07(− 2.39–5.62) and, miR-223 1.71(− 0.39–4.43). Differences were assessed by Mann–Whitney where, all miRNAs showed a highly significant difference between study and control groups, except miR-140b showed a non-significant difference (Fig. 2).
Circulating miRNAs predictive significance as biomarkers by ROC analysis
Receiver operating curve (ROC) including Area Under Curve (AUC) and probability levels were presented in Table 3. The ROC curve data from Table 3 indicated that miR-145, miR-182, miR-133a, miR-205, miR-21, miR-155, miR-126, miR-146A, miR-200b, miR-135b revealed a highly significant (p < 0.001***) and valuable biomarkers with the highest AUCs of 0.959, 0.959, 0.863, 0.836, 0.767, 0.767, 0.767, 0.767, 0.740, and 0.712 respectively.
Correlation analysis of circulating miRNAs differential expression levels and the CAD patients’ clinical characteristics
The 14 selected circulating miRNAs showed various distribution among all CAD patients. The Spearman’s rank correlation of the 14 selected plasma miRNAs in both control and CAD was evaluated and presented in Fig. 3. There were a strong association between some of the miRNAs understudy in CAD patients with Spearman’s correlation coefficient of 0.59 and more and a two-tailed significance p < 0.0001 (miR-182 and miR-145: r = 0.820***; miR-182 and miR-205: r = 0.693***; miR-145 and miR-205: r = 0.678***; miR-146a vs. miR-182: r = 0.619***; miR-146a vs. miR-145: r = 0.639***; miR-21 vs miR-145: r = 0.595***).
Considering the relation between miRNAs and the clinical data shown in Table 4, most of the studied miRNAs showed positive statistically significant relation with age except miR-140b, miR-196b, and miR-223. MiR-21, miR-126, miR-135b, miR-155, and miR-182 significantly linked with smoking. MiR-133a and miR-182 showed significant association with family history. All miRNAs under study except miR-208 revealed a statistically significant relation with dyslipidemia. MiR-126 and miR-155 showed significance with BMI grade, while only miR-133a showed significance with the obese patients. MiR-140b, miR-182, miR-196b, and miR-208 revealed positive statistically significant relation with hypertension, while miR-21 and miR-145 showed significance with ischemic heart disease. Finally, miR-135b and miR-140b showed a significant correlation concerning the Wall Motion Severity Index.
Function and pathway enrichment analysis of circulating miRNAs DE in CAD
For identifying all the pathways targeted by DE circulating miRNAs in CAD, a pathway enrichment analysis based on annotated gene targets in GO was performed. The databases were used to assess the 14 miRNAs under study regulatory functions and for identifying the molecular pathways for the miRNAs under study. We used the KEGG pathway database to perform the functional pathway analysis. Enrichment of specific pathways revealed pathways relevant to the fatty acid biosynthesis, ECM-receptor interaction, proteoglycans in cancer, and adherens junction were found as shown in Fig. 4. The fatty acid biosynthesis and ECM-receptor interaction pathways were significantly enriched in CAD patients (Fig. 4B).
The GO biological processes related to CAD pathogenesis were found to be distinctly enriched in our analysis as the enriched pathways were associated with negative regulation of transport, regulation of cardiomyocyte differentiation, negative regulation of cytokine production, regulation of muscle cell differentiation, regulation of smooth muscle cell proliferation, miRNA-mediated gene silencing by inhibition of translation and gene silencing by miRNA as represented in Fig. 5A.
To assure quality control and investigate association analysis, the circulating miRNAs understudy was enriched using DisGeNET, collected and grouped into clusters as shown in Fig. 5B based on the top enriched clusters and their membership similarities where it identified cardiovascular morbidity as one of the top clusters among which our circulating miRNAs are involved.
MiRNA‐mRNA regulatory network construction
Our network analysis identified the relationship between the circulating miRNAs under study and their target genes. Our miRNA-target gene network comprised 14 microRNAs and 295 target genes then filtered with a minimum of 3 shared targets that revealed a final of 87 target genes using miRTargetLink 2.0 (https://ccb-web.cs.uni-saarland.de/mirtargetlink/network.php) (Fig. 6). The circulating miRNAs understudy and their targeted genes were related to the biological processes known to be involved in CAD pathogenesis, such as fatty acid biosynthesis, ECM-receptor interaction, proteoglycans in cancer and adherens junction, negative regulation of transport, regulation of cardiomyocyte differentiation, negative regulation of cytokine production, regulation of muscle cell differentiation, regulation of smooth muscle cell proliferation, miRNA-mediated gene silencing by inhibition of translation and gene silencing by miRNA.
Among the critical genes involved in CAD were the SMAD genes that are targeted by six of our circulating miRNAs (miR-135b, miR145, miR146a, miR-155, miR182, and miR-205) for proteins involved in ECM remodeling, cell differentiation, endocardial and epicardial EMT, neural crest migration, and maintenance of cardiovascular structure and function.
Finally, target genes regulated by the circulating miRNAs understudy were also correlated with the FOXO signaling pathway, such as FOXO1 (miR-21 and miR-135b), FOXO3 (miR-21, miR-126, miR155, miR-182), and genes related to the adherens junction pathway, including EGFR (miR-21, miR-133a, miR145, miR-146a and miR-155), TGFBR1 (miR-21, miR145, miR-196b). Finally, several genes were involved in the proteoglycans in cancer pathway that regulated MAPK1, FN1, FZD4, CTNNB1, RDX, MSN, SDC2, ACTG1, and IGF1R. The proteoglycans in cancer pathway modulate the dynamics and kinetics of various ligand-receptor interactions that appear to play a role in CAD pathogenesis.
Although enormous progress has been achieved to diagnose and treat CAD with invasive techniques, serious cardiovascular events occur to a large percentage of patients with this disease [22, 23]. These serious events can be partly referred to as unraveled molecular events that lead to CAD pathogenesis, most likely involving atherosclerosis and genetic factors [23,24,25]. In the search for reliable biomarkers for CAD, circulating miRNAs biostable nature, encouraged research in this area aiming to use it as non-invasive biomarkers . Given a possible clinical transferability of our results, we have isolated circulating miRNAs from EDTA-plasma, for investigating a panel of 14 circulating miRNAs shown in Table 1 relying upon the previously reported results for the sensitivity of the qRT-PCR for the extracted miRNAs from plasma [27, 28].
In accordance with our results, Ren et al., Tsai et al., and Li et al., [12, 29, 30] reported the up-regulation of miR-21 in CAD. This miR-21 up-regulation could be due to the associated effects of vascular wall-shear stress on the endothelium and oxidative stress [31,32,33] and due to the effect of the oscillatory shear stress that contributes to the vascular endothelium proinflammatory responses. Ren et al., Liu et al., Jansen et al., Wagner et al., and D'Alessandra et al. [12, 34,35,36,37] showed upregulation of miR-126 in their CAD research. MiR-126 is responsible for endothelial cell repair and vascular development, and the endothelial cells is enriched with it [38, 39]. Xu et al. showed upregulation of miR-135b among CAD patients compared to controls. MiR-135b targets the MEF2C gene, which is mainly involved in cellular homeostasis, cell proliferation, and migration in the cardiovascular system, which affects the cells phenotype [39,40,41,42]. Maciejak et al., Zhu et al., and Choteau et al. [43,44,45] documented upregulation of miR-145 in CAD. MiR-145 is abundant in vascular smooth muscles. Its expression is dysregulated in atherosclerotic vessels . Niculescu et al. and Dégano et al. [47, 48] reported the upregulation of miR-146a among CAD patients. MiR-146a is implicated in both inflammation and lipid homeostasis [48, 49]. MiR-146a functions by its inhibitory effect on oxidized low-density lipoproteins and inflammatory response , thus affecting the pathogenesis of atherosclerosis . Zhu et al. documented in their work the upregulation of miR-182 . Xu et al. reported upregulation of miR-205 resembling our findings. MiR-205 is recently discovered to decrease cellular proliferation, hinders invasion, and increase apoptosis [52, 53]. Liu et al., Schulte et al., and Shan et al. [34, 54, 55] reported upregulation of miR-223, resembling our study results. MiR-223 is thought to regulate endothelial cells inflammation and appears to be associated with HDL [56, 57]. Magenta et al.  reported upregulation of miR-200b and highlighted that it is overexpressed in atherosclerosis, ischemic muscles, and vascular dysfunction. Fichtlscherer et al. and Weber et al. [59, 60] reported downregulation of miR-155 as our results. MiR-155 is known to be implicated in inflammatory responses where it strengthen inflammation and sustain macrophages [61, 62]. Finally, Patterson et al. showed downregulation of miR-133a and 208a in CAD patients .
On the contrary to our results, D'Alessandra et al., and Liu et al. [37, 64] reported upregulation of miR-133a and miR-208a, which was significantly downregulated in our study. Fichtlscherer et al.  reported downregulation of miR-126 and miR-145, while Weber et al., Gao et al., Ying et al., and Wagner et al. [60, 65,66,67] detected downregulation of miR-145. Ying et al.  reported downregulation of miR-196 while Wagner et al.  showed downregulation of miR-223. MiR-145 and miR-182 were the most upregulated miRNAs in our study, although miR-145 is not usually upregulated in CAD patients.
From the abovementioned, we deduce that there is no consensus on the relative expression signature of circulating miRNAs in CAD. So, we should interpret miRNA results with caution due to these contradicting results. These contradictions can be explained by which type of body fluid was used prior to miRNA extraction, how the sample was prepared and preserved, which platform was used for the analysis. Going into more sophisticated details, RNA extraction method itself can affect the concentration and quality of miRNAs extracted . Also, the normalization strategy whether mono or multiple endogenous controls were used is important [69,70,71,72,73]. Moreover, during sample collection and preparation step, centrifugation is a necessary procedure for blood. The centrifugation helps in starting with high-quality plasma for miRNA extraction . These methodological variations could lead to conflicting results between different studies. In our opinion, standardization of various methodologies among all studies investigating circulating miRNAs can help in solving the contradicting results problem in the future.
According to ROC analysis results for unraveling the discriminating power of the circulating miRNAs under study, miR-145, miR-182, miR-205, and miR-133a were found to be highly predictive as potential biomarkers for discriminating CAD patients from controls as their AUCs values was above 0.80 . MiR-182 and miR-205 are recently linked miRNAs in cardiovascular disease. Thus, these four circulating miRNAs may be used as a panel for the detecting underlined CAD pathogenesis. However, the sensitivity and specificity of this four circulating miRNAs panel need to be further investigated in a larger cohort.
Considering the relation between miRNAs and the clinical data, most of the studied miRNAs showed a statistically significant relation with age. Only miR-133a and miR-155 showed a significant inverse correlation with age, and this was consistent with the results of Fichtlscherer et al. . In the same consensus, miR-223 was directly correlated with age, as shown by Schulte and his colleagues . On the contrary, Ali et al.  showed different results with no correlation between their miRNAs understudy and age. Regarding special habits, very few studies studied the role of smoking with circulating miRNAs in CAD. Our results showed that miR-21, miR-126, miR-135b, miR-155, and miR-182 was significantly correlated with smoking. In contrast to our study results, miR-145 was significantly associated with smoking, as reported by Gao and his colleagues . Although our study reported a significant correlation between miR-133a and family history, another Egyptian study by Turky et al. didn't correlate miR-133a with family history in CAD . All miRNAs under study except miR-208 revealed a statistically significant relation with dyslipidemia. Following our results, ElShafea et al. in Egypt reported a significant reverse correlation with dyslipidemia . Faccini et al. reported upregulation of miR-140 and that its antagonism could be a new therapeutic strategy for treating hypercholesterolemia and atherosclerosis . In contrast, Fujii et al. contradicted our results and reported that miR-126 wasn't correlated with dyslipidemia . Considering obesity, our study and another Egyptian study done by Turky et al. showed that miR-133a showed significance with the obesity in CAD patients . Other miRNAs in our study were not correlated with obesity. In comparison, miR-126 and miR-155 showed significance with BMI grade. Jusic et al. reported the association of miR-21 with hypertension in CAD patients , which was not the case in our study, but miR-140b, miR-182, miR-196b, and miR-208 showed to be correlated with hypertension. Regarding ischemic heart disease, Jansen et al. reported that miR-126 correlates with IHD in CAD patients , which was not the case also in our study. Instead, miR-21 and miR-145 showed a significant correlation with IHD in CAD patients. Finally, the observed correlation between miR-135b and miR-140b and WMSI was not reported before in the literature. This finding may be attributed to the fact that miR-135b and miR-140b overexpression has a role in blood vessel endothelial cell migration and proliferation, impaired cardiac conduction system activity, enhanced cardiomyocytes apoptosis, and decreased resistance to reactive oxygen species (ROS) that can cause changes in the vessel walls [83, 84].
The combined action of the 14 studied miRNAs revealed number of pathways regulating fatty acid biosynthesis and ECM-receptor interaction (Fig. 4). As the pathways identified in our study have been proved to be associated with CAD [85, 86], we can deduce that this group of miRNAs are mostly implicated in the pathogenesis of CAD with their predicted and/or validated function and biomarker type are summarized in Table 5.
MiRNAs function via modulating the expression of its target messenger RNA, thereby affecting important biological processes . By investigating the potential biological role of miRNA-specific target genes in our study, most of the target genes were enriched in the biological process of negative regulation of transport, regulation of cardiomyocyte differentiation, negative regulation of cytokine production, regulation of muscle cell differentiation, regulation of smooth muscle cell proliferation, miRNA-mediated gene silencing by inhibition of translation and gene silencing by miRNA. These results emphasized that our deregulated miRNAs under study play an important role in CAD and is participating in various signaling pathways that is related to circulatory function. Circulating miRNAs shown to affect target mRNA expression in different cells , So, using miRTargetLink 2.0 as shown in (Fig. 6), we predicted the target genes for the miRNAs understudy to understand their biological roles in CAD. We found that the 14 miRNAs understudy may affect several aspects of atherosclerotic plaques, such as inflammation, hypoxia, angiogenesis, inflammation, apoptosis, and ECM degradation (Table 5). They may also regulate several key-signaling pathways in atherosclerotic plaques, such as pathways involving toll-like receptor-4 (TLR-4), hypoxia-inducible factor 1a, and (HIF-1a), transforming growth factor-b (TGF-b), and FOXO signaling pathway.
Concerning our study limitations, this is a mono center study involving a limited number of patients and we investigated only 14 human miRNAs. So, we recommend doing multicentric studies across different geographical locations in the region with larger study populations. Also, we cannot exclude that other miRNAs not investigated in our study is not implicated in CAD pathogenesis. Moreover, several confounders like age, smoking, family history, and dyslipidemia differed between the CAD and control groups. So, the expression of these miRNAs could have been affected by these confounders. Thus further in vitro, in vivo, functional and clinical validation studies could help in better understanding of the precise role of miRNAs in CAD and in validating this study findings.
In conclusion, the results of this study identified a differentially expressed circulating miRNAs signature that can discriminate CAD patients from control subjects. These results provide new insights into the pivotal role of miRNAs expression associated with CAD pathogenesis. The potential diagnostic value of circulating miRNAs has been shown in CAD patients as depicted through our discussion. Our study results extends these findings and confirm that CAD patients show specific circulating miRNAs signature. Also, we revealed novel findings regarding correlation with clinical data where we reported that miR-133a and miR-182 showed significant relation with family history. MiR-140b, miR-182, miR-196b, and miR-208 revealed a positive statistically significant link with hypertension, while miR-21 and miR-145 showed significance with ischemic heart disease. Finally, miR-135b and miR-140b showed to be correlated with WMSI. However, owing to the current overlap of the signatures pinpointed from various studies, we recommend further studies in relation to the miRNAs discriminating power in CAD. These future studies should preferably standardize the laboratory methodology, address larger population size, implementing functional and clinical validation studies to help better understanding the underlying clinical significance and miRNAs role in CAD development. Finally, we can declare that despite barriers to implementing miRNA-based studies in CAD, our research results foresee it as promising non-invasive biomarkers in CAD. To the best of our knowledge, this is the first study in Egypt to assess a CAD mQ11iRNAs panel encompassing 14 biomarkers.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, Jessup M, Konstam MA, Mancini DM, Michl K, Oates JA. 2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines developed in collaboration with the international society for heart and lung transplantation. J Am Coll Cardiol. 2009;53(15):e1-90.
Gomes CP, Ágg B, Andova A, Arslan S, Baker A, Barteková M, Beis D, Betsou F, Bezzina Wettinger S, Bugarski B, Condorelli G. Catalyzing transcriptomics research in cardiovascular disease: the CardioRNA COST action CA17129. Non-Coding RNA. 2019;5(2):31.
Hassanin A, Hassanein M, Bendary A, Maksoud MA. Demographics, clinical characteristics, and outcomes among hospitalized heart failure patients across different regions of Egypt. Egypt Heart J. 2020;72(1):1–9.
Lawlor DA, Smith GD, Leon DA, Sterne JA, Ebrahim S. Secular trends in mortality by stroke subtype in the 20th century: a retrospective analysis. Lancet. 2002;360(9348):1818–23.
Sajjadieh A, Hekmatnia A, Keivani M, Asoodeh A, Pourmoghaddas M, Sanei H. Diagnostic performance of 64-row coronary CT angiography in detecting significant stenosis as compared with conventional invasive coronary angiography. ARYA Atheroscler. 2013;9(2):157.
Silverio A, Cavallo P, De Rosa R, Galasso G. Big health data and cardiovascular diseases: a challenge for research, an opportunity for clinical care. Front Med. 2019;25(6):36.
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281–97.
Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2010;39(suppl_1):D152–7.
Bajan S, Hutvagner G. Regulation of miRNA processing and miRNA mediated gene repression in cancer. Microrna. 2014;3(1):10–7.
Min PK, Chan SY. The biology of circulating microRNA s in cardiovascular disease. Eur J Clin Invest. 2015;45(8):860–74.
Zhou SS, Jin JP, Wang JQ, Zhang ZG, Freedman JH, Zheng Y, Cai L. miRNAS in cardiovascular diseases: potential biomarkers, therapeutic targets and challenges. Acta Pharmacol Sin. 2018;39(7):1073–84.
Ren J, Zhang J, Xu N, Han G, Geng Q, Song J, Li S, Zhao J, Chen H. Signature of circulating microRNAs as potential biomarkers in vulnerable coronary artery disease. PLoS One. 2013;8(12):e80738.
Jamaluddin MS, Weakley SM, Zhang L, Kougias P, Lin PH, Yao Q, Chen C. miRNAs: roles and clinical applications in vascular disease. Expert Rev Mol Diagn. 2011;11(1):79–89.
Tijsen AJ, Pinto YM, Creemers EE. Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases. Am J Physiol-Heart Circ Physiol. 2012;303(9):H1085–95.
Melak T, Baynes HW. Circulating microRNAs as possible biomarkers for coronary artery disease: a narrative review. Ejifcc. 2019;30(2):179.
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13.
Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545–51.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.
Kern F, Aparicio-Puerta E, Li Y, Fehlmann T, Kehl T, Wagner V, Ray K, Ludwig N, Lenhof HP, Meese E, Keller A. miRTargetLink 2.0— interactive miRNA target gene and target pathway networks. Nucleic Acids Res. 2021;49(W1):W409–16.
Faul F, Erdfelder E, Lang AG, Buchner A. G* power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91.
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25(4):402–8. https://doi.org/10.1006/meth.2001.1262.
Vasan RS, Sullivan LM, Wilson PW, Sempos CT, Sundström J, Kannel WB, Levy D, D’agostino RB. Relative importance of borderline and elevated levels of coronary heart disease risk factors. Ann Intern Med. 2005;142(6):393–402.
Cresci S, Depta JP, Lenzini PA, Li AY, Lanfear DE, Province MA, Spertus JA, Bach RG. Cytochrome p450 gene variants, race, and mortality among clopidogrel-treated patients after acute myocardial infarction. Circ Cardiovasc Genet. 2014;7(3):277–86.
Mega JL, Close SL, Wiviott SD, Shen L, Walker JR, Simon T, Antman EM, Braunwald E, Sabatine MS. Genetic variants in ABCB1 and CYP2C19 and cardiovascular outcomes after treatment with clopidogrel and prasugrel in the TRITON–TIMI 38 trial: a pharmacogenetic analysis. Lancet. 2010;376(9749):1312–9.
Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, Chasman DI, Baber U, Mehran R, Rader DJ, Fuster V. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med. 2016;375(24):2349–58.
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33.
Meltzer PS. Small RNAs with big impacts. Nature. 2005;435(7043):745–6.
Tsui NB, Ng EK, Lo YD. Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem. 2002;48(10):1647–53.
Tsai P-C, Liao Y-C, Wang Y-S, et al. Serum microRNA-21 and microRNA-221 as potential biomarkers for cerebrovascular disease. J Vasc Res. 2013;50:346–54.
Li T, Cao H, Zhuang J, et al. Identification of miR-130a, miR-27b and miR-210 as serum biomarkers for atherosclerosis obliterans. Clin Chim Acta. 2011;412:66–70.
Fleissner F, Jazbutyte V, Fiedler J, et al. Short communication: asymmetric dimethylarginine impairs angiogenic progenitor cell function in patients with coronary artery disease through a microRNA-21-dependent mechanism. Circ Res. 2010;107:138–43.
Weber M, Baker MB, Moore JP, et al. MiR-21 is induced in endothelial cells by shear stress and modulates apoptosis and eNOS activity. Biochem Biophys Res Commun. 2010;393:643–8.
Zhou J, Wang K-C, Wu W, et al. MicroRNA-21 targets peroxisome proliferators-activated receptor-alpha in an autoregulatory loop to modulate flow-induced endothelial inflammation. Proc Natl Acad Sci U S A. 2011;108:10355–60.
Liu W, Ling S, Sun W, Liu T, Li Y, Zhong G, Zhao D, Zhang P, Song J, Jin X, Xu Z. Circulating microRNAs correlated with the level of coronary artery calcification in symptomatic patients. Sci Rep. 2015;5(1):1.
Jansen F, Yang X, Proebsting S, et al. MicroRNA expression in circulating microvesicles predicts cardiovascular events in patients with coronary artery disease. J Am Heart Assoc. 2014;3:e001249.
Wagner J, Riwanto M, Besler C, et al. Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs. Arterioscler Thromb Vasc Biol. 2013;33:1392–400.
D’Alessandra Y, Carena MC, Spazzafumo L, et al. Diagnostic potential of plasmatic microRNA signatures in stable and unstable angina. PLoS ONE. 2013;8:e80345.
Kuhnert F, Mancuso MR, Hampton J, et al. Attribution of vascular phenotypes of the murine Egfl7 locus to the microRNA miR-126. Development. 2008;135:3989–93.
Maiti D, Xu Z, Duh EJ. Vascular endothelial growth factor induces MEF2C and MEF2-dependent activity in endothelial cells. Invest Ophthalmol Vis Sci. 2008;49(8):3640–8.
Xu Z, Han Y, Liu J, Jiang F, Hu H, Wang Y, Liu Q, Gong Y, Li X. MiR-135b-5p and MiR-499a-3p promote cell proliferation and migration in atherosclerosis by directly targeting MEF2C. Sci Rep. 2015;5(1):1–5.
Potthoff MJ, Olson EN. MEF2: a central regulator of diverse developmental programs. 2007;134(23):4131–4140. https://doi.org/10.1242/dev.008367.
Lin Q, Lu J, Yanagisawa H, Webb R, Lyons GE, Richardson JA, Olson EN. Requirement of the MADS-box transcription factor MEF2C for vascular development. Development. 1998;125(22):4565–74.
Maciejak A, Kiliszek M, Opolski G, Segiet A, Matlak K, Dobrzycki S, Tulacz D, Sygitowicz G, Burzynska B, Gora M. miR-22-5p revealed as a potential biomarker involved in the acute phase of myocardial infarction via profiling of circulating microRNAs. Mol Med Rep. 2016;14(3):2867–75.
Zhu Y, Lin Y, Yan W, Sun Z, Jiang Z, Shen B, Jiang X, Shi J. Novel biomarker microRNAs for subtyping of acute coronary syndrome: a bioinformatics approach. Biomed Res Int. 2016;1:2016.
Choteau SA, Torres LF, Barraclough JY, Elder AM, Martínez GJ, Fan WY, Shrestha S, Ong KL, Barter PJ, Celermajer DS, Rye KA. Transcoronary gradients of HDL-associated MicroRNAs in unstable coronary artery disease. Int J Cardiol. 2018;15(253):138–44.
Urbich C, Kuehbacher A, Dimmeler S. Role of microRNAs in vascular diseases, inflammation, and angiogenesis. Cardiovasc Res. 2008;79:581–8.
Niculescu LS, Simionescu N, Sanda GM, Carnuta MG, Stancu CS, Popescu AC, Popescu MR, Vlad A, Dimulescu DR, Simionescu M, Sima AV. MiR-486 and miR-92a identified in circulating HDL discriminate between stable and vulnerable coronary artery disease patients. PLoS ONE. 2015;10(10):e0140958.
Dégano IR, Camps-Vilaró A, Subirana I, García-Mateo N, Cidad P, Muñoz-Aguayo D, Puigdecanet E, Nonell L, Vila J, Crepaldi FM, de Gonzalo-Calvo D. Association of circulating microRNAs with coronary artery disease and usefulness for reclassification of healthy individuals: the REGICOR study. J Clin Med. 2020;9(5):1402.
Darabi F, Aghaei M, Movahedian A, Pourmoghadas A, Sarrafzadegan N. The role of serum levels of microRNA-21 and matrix metalloproteinase-9 in patients with acute coronary syndrome. Mol Cell Biochem. 2016;422(1):51–60.
Darabi F, Aghaei M, Movahedian A, Elahifar A, Pourmoghadas A, Sarrafzadegan N. Association of serum microRNA-21 levels with Visfatin, inflammation, and acute coronary syndromes. Heart Vessels. 2017;32(5):549–57.
Zhu L, Chen T, Ye W, Wang JY, Zhou JP, Li ZY, Li CC. Circulating miR-182-5p and miR-5187-5p as biomarkers for the diagnosis of unprotected left main coronary artery disease. J Thorac Dis. 2019;11(5):1799.
Xu Y, Guo W, Zeng D, Fang Y, Wang R, Guo D, Qi B, Xue Y, Xue F, Jin Z, Li Y. Inhibiting miR-205 alleviates cardiac ischemia/reperfusion injury by regulating oxidative stress, mitochondrial function, and apoptosis. Oxid Med Cell Longev. 2021;29:2021.
Feng L, Wei J, Liang S, Sun Z, Duan J. miR-205/IRAK2 signaling pathway is associated with urban airborne PM2.5-induced myocardial toxicity. Nanotoxicology. 2020;14(9):1198–212.
Schulte C, Molz S, Appelbaum S, Karakas M, Ojeda F, Lau DM, Hartmann T, Lackner KJ, Westermann D, Schnabel RB, Blankenberg S. miRNA-197 and miRNA-223 predict cardiovascular death in a cohort of patients with symptomatic coronary artery disease. PLoS ONE. 2015;10(12):e0145930.
Shan Z, Qin S, Li W, Wu W, Yang J, Chu M, Li X, Huo Y, Schaer GL, Wang S, Zhang C. An endocrine genetic signal between blood cells and vascular smooth muscle cells: role of microRNA-223 in smooth muscle function and atherogenesis. J Am Coll Cardiol. 2015;65(23):2526–37.
Vickers KC, Palmisano BT, Shoucri BM, et al. MicroRNAs are trans- ported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011;13:423–33.
Tabet F, Vickers KC, Cuesta Torres LF, et al. HDL-transferred microRNA-223 regulates ICAM-1 expression in endothelial cells. Nat Commun. 2014;5:3292.
Magenta A, Ciarapica R, Capogrossi MC. The emerging role of miR-200 family in cardiovascular diseases. Circ Res. 2017;120(9):1399–402.
Fichtlscherer S, De Rosa S, Fox H, et al. Circulating microRNAs in patients with coronary artery disease. Circ Res. 2010;107:677–84.
Weber M, Baker MB, Patel RS, et al. MicroRNA expression profile in CAD patients and the impact of ACEI/ARB. Cardiol Res Pract. 2011;2011:1–5.
Nazari-Jahantigh M, Wei Y, Noels H, Akhtar S, Zhou Z, Koenen RR, Heyll K, Gremse F, Kiessling F, Grommes J, Weber C. MicroRNA-155 promotes atherosclerosis by repressing Bcl6 in macrophages. J Clin Investig. 2012;122(11):4190–202.
Wei Y, Zhu M, Corbalán-Campos J, et al. Regulation of Csf1r and Bcl6 in macrophages mediates the stage-specific effects of microRNA-155 on atherosclerosis. Arterioscler Thromb Vasc Biol. 2015;35:796–803.
Patterson AJ, Song MA, Choe D, Xiao D, Foster G, Zhang L. Early detection of coronary artery disease by micro-RNA analysis in asymptomatic patients stratified by coronary CT angiography. Diagnostics. 2020;10(11):875.
Liu H, Yang N, Fei Z, et al. Analysis of plasma miR-208a and miR-370 expression levels for early diagnosis of coronary artery disease. Biomed Rep. 2016;5:332–6.
Gao H, Guddeti RR, Matsuzawa Y, et al. Plasma levels of microRNA- 145 are associated with severity of coronary artery disease. PLoS ONE. 2015;10:e0123477.
Ying D, Yang SH, Sha L, et al. Circulating microRNAs as novel diagnostic biomarkers for very early-onset (≤40 years) coronary artery disease. Biomed Environ Sci. 2016;29:545–54.
Wagner J, Riwanto M, Besler C, Knau A, Fichtlscherer S, Röxe T, Zeiher AM, Landmesser U, Dimmeler S. Characterization of levels and cellular transfer of circulating lipoprotein-bound microRNAs. Arterioscler Thromb Vasc Biol. 2013;33(6):1392–400.
Faccini J, Ruidavets JB, Cordelier P, Martins F, Maoret JJ, Bongard V, Ferrières J, Roncalli J, Elbaz M, Vindis C. Circulating miR-155, miR-145 and let-7c as diagnostic biomarkers of the coronary artery disease. Sci Rep. 2017;7(1):1.
Farina NH, Wood ME, Perrapato SD, Francklyn CS, Stein GS, Stein JL, Lian JB. Standardizing analysis of circulating microRNA: clinical and biological relevance. J Cell Biochem. 2014;115(5):805–11.
Brown RA, Epis MR, Horsham JL, Kabir TD, Richardson KL, Leedman PJ. Total RNA extraction from tissues for microRNA and target gene expression analysis: not all kits are created equal. BMC Biotechnol. 2018;18(1):1–1.
Vigneron N, Meryet-Figuière M, Guttin A, Issartel JP, Lambert B, Briand M, Louis MH, Vernon M, Lebailly P, Lecluse Y, Joly F. Towards a new standardized method for circulating miRNAs profiling in clinical studies: Interest of the exogenous normalization to improve miRNA signature accuracy. Mol Oncol. 2016;10(7):981–92.
Wang K, Yuan Y, Cho JH, McClarty S, Baxter D, Galas DJ. Comparing the MicroRNA spectrum between serum and plasma. PLoS ONE 2012;7(7):e41561. https://doi.org/10.1371/journal.pone.0041561.
Schwarzenbach H, Da Silva AM, Calin G, Pantel K. Data normalization strategies for microRNA quantification. Clin Chem. 2015;61(11):1333–42.
Nikas JB, Low WC. ROC-supervised principal component analysis in connection with the diagnosis of diseases. Am J Transl Res. 2011;3(2):180.
Fichtlscherer S, De Rosa S, Fox H, Schwietz T, Fischer A, Liebetrau C, Weber M, Hamm CW, Röxe T, Müller-Ardogan M, Bonauer A. Circulating microRNAs in patients with coronary artery disease. Circ Res. 2010;107(5):677–84.
Ali W, Mishra S, Rizvi A, Perrone Ma, Tasleem M, Wamique M, Sethi R, Pradhan A. Diagnostic value of circulating MicroRNAs for middle aged coronary artery disease patients: a case-control study. J Clin Diagn Res. 2021;15(3):5–10.
Gao H, Guddeti RR, Matsuzawa Y, Liu LP, Su LX, Guo D, Nie SP, Du J, Zhang M. Plasma levels of microRNA-145 are associated with severity of coronary artery disease. PLoS ONE. 2015;10(5):e0123477.
Turky HF, Emam WA, Shalaby SM, Kandil NT. Plasma MicroRNA-133a as a potential predictor for coronary artery stenosis severity. Zagazig Univ Med J. 2020;26(1):64–74.
Elshafae MM, Sabry JH, Salem MA, Elshafee HM. MicroRNA-155 in patients with chronic stable angina. Ann Appl Bio-Sci. 2017;4(1):A74-82.
Fujii S, Sugiura T, Dohi Y, Ohte N. MicroRNA in atherothromobosis: is it useful as a disease marker? Thromb J. 2016;14(1):141–3.
Jusic A, Devaux Y. EU-CardioRNA COST Action (CA17129) noncoding RNAs in hypertension. Hypertension. 2019;74(3):477–92.
Jansen F, Schäfer L, Wang H, Schmitz T, Flender A, Schueler R, Hammerstingl C, Nickenig G, Sinning JM, Werner N. Kinetics of circulating micro RNA s in response to cardiac stress in patients with coronary artery disease. J Am Heart Assoc. 2017;6(8):e005270. https://doi.org/10.1161/JAHA.116.005270
Moris D, Spartalis M, Tzatzaki E, Spartalis E, Karachaliou GS, Triantafyllis AS, Karaolanis GI, Tsilimigras DI, Theocharis S. The role of reactive oxygen species in myocardial redox signaling and regulation. Ann Transl Med. 2017; 5(16).
Werner TR, Kunze AC, Stenzig J, Eschenhagen T, Hirt MN. Blockade of miR-140-3p prevents functional deterioration in afterload-enhanced engineered heart tissue. Sci Rep. 2019;9(1):1.
Frangogiannis NG. The inflammatory response in myocardial injury, repair, and remodelling. Nat Rev Cardiol. 2014;11(5):255–65.
Heliste J, Jokilammi A, Paatero I, Chakroborty D, Stark C, Savunen T, Laaksonen M, Elenius K. Receptor tyrosine kinase profiling of ischemic heart identifies ROR1 as a potential therapeutic target. BMC Cardiovasc Disord. 2018;18(1):1–2.
O’Brien J, Hayder H, Zayed Y, Peng C. Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol. 2018;3(9):402.
Sohel MH. Extracellular/circulating microRNAs: release mechanisms, functions and challenges. Achiev Life Sci. 2016;10(2):175–86.
Ahlin F, Arfvidsson J, Vargas KG, Stojkovic S, Huber K, Wojta J. MicroRNAs as circulating biomarkers in acute coronary syndromes: a review. Vascul Pharmacol. 2016;1(81):15–21.
Liu N, Bezprozvannaya S, Williams AH, Qi X, Richardson JA, Bassel-Duby R, Olson EN. microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in the heart. Genes Dev. 2008;22(23):3242–54.
Laffont B, Rayner KJ. MicroRNAs in the pathobiology and therapy of atherosclerosis. Can J Cardiol. 2017;33(3):313–24.
Taurino C, Miller WH, McBride MW, McClure JD, Khanin R, Moreno MU, Dymott JA, Delles C, Dominiczak AF. Gene expression profiling in whole blood of patients with coronary artery disease. Clin Sci. 2010;119(8):335–43.
Cordes KR, Sheehy NT, White MP, et al. miR-145 and miR-143 regulate smooth muscle cell fate and plasticity. Nature. 2009;460:705–10.
Wei Y, Nazari-Jahantigh M, Neth P, et al. MicroRNA-126, , àí145, and , àí155: a therapeutic triad in atherosclerosis? Arterioscler Thromb Vasc Biol. 2013;33:449–54.
Taganov KD, Boldin MP, Chang K-J, et al. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A. 2006;103:12481–6.
Yang K, He YS, Wang XQ, et al. MiR-146a inhibits oxidized low- density lipoprotein-induced lipid accumulation and inflammatory response via targeting toll-like receptor 4. FEBS Lett. 2011;585:854–60.
Androulidaki A, Iliopoulos D, Arranz A, et al. The kinase Akt1 con- trols macrophage response to lipopolysaccharide by regulating microRNAs. Immunity. 2009;31:220–31.
Du F, Yu F, Wang Y, et al. MicroRNA-155 deficiency results in decreased macrophage inflammation and attenuated atherogen- esis in apolipoprotein E-deficient mice. Arterioscler Thromb Vasc Biol. 2014;34:759–67.
Li N, Hwangbo C, Jaba IM, Zhang J, Papangeli I, Han J, Mikush N, Larrivée B, Eichmann A, Chun HJ, Young LH. miR-182 modulates myocardial hypertrophic response induced by angiogenesis in heart. Sci Rep. 2016;6(1):1–5.
Stather PW, Sylvius N, Sidloff DA, Dattani N, Verissimo A, Wild JB, Butt HZ, Choke E, Sayers RD, Bown MJ. Identification of microRNAs associated with abdominal aortic aneurysms and peripheral arterial disease. J Br Surg. 2015;102(7):755–66.
Wu Q, Chen Q, Wang J, Fan D, Zhou H, Yuan Y, Shen D. Long non-coding RNA Pvt1 modulates the pathological cardiac hypertrophy via miR-196b-mediated OSMR regulation. Cell Signal. 2021;1(86):110077.
Zhang F, Cheng N, Du J, Zhang H, Zhang C. MicroRNA-200b-3p promotes endothelial cell apoptosis by targeting HDAC4 in atherosclerosis. BMC Cardiovasc Disord. 2021;21(1):1–2.
Chistiakov DA, Orekhov AN, Bobryshev YV. Cardiac-specific miRNA in cardiogenesis, heart function, and cardiac pathology (with focus on myocardial infarction). J Mol Cell Cardiol. 2016;1(94):107–21.
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Article processing charge (APC) was funded by the Egyptian Science, Technology & Innovation Funding Authority (STDF) via the Springer Nature - Egyptian open access agreement for supporting researchers inside Egypt.
Ethics approval and consent to participate
The study was approved by the Suez Canal University, Faculty of Medicine, Ethics Committee in Ismailia, Egypt (Approval No. 4501) and conducted according to the Declaration of Helsinki’s guidelines. Informed consent was obtained from all individual participants included in the study.
On behalf of all authors, the corresponding author states that there is noCompeting interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Abdallah, H.Y., Hassan, R., Fareed, A. et al. Identification of a circulating microRNAs biomarker panel for non-invasive diagnosis of coronary artery disease: case–control study. BMC Cardiovasc Disord 22, 286 (2022). https://doi.org/10.1186/s12872-022-02711-9
- Circulating miRNAs
- Coronary artery disease
- CAD biomarker