Abstract

   Atherosclerosis is mediated by various factors and plays an important
   pathological foundation for cardiovascular and cerebrovascular
   diseases. Abnormal vascular smooth muscle cells (VSMCs) proliferation
   and migration have an essential role in atherosclerotic lesion
   formation. Circular RNAs (circRNA) have been widely detected in
   different species and are closely related to various diseases. However,
   the expression profiles and molecular regulatory mechanisms of circRNAs
   in VSMCs are still unknown. We used high-throughput RNA-seq as well as
   bioinformatics tools to systematically analyze circRNA expression
   profiles in samples from different VSMC phenotypes. Polymerase chain
   reaction (PCR), Sanger sequencing, and qRT-PCR were performed for
   circRNA validation. A total of 22191 circRNAs corresponding to 6273
   genes (host genes) in the platelet-derived growth factor (PDGF-BB)
   treated group, the blank control group or both groups, were detected,
   and 112 differentially expressed circRNAs were identified between the
   PDGF-BB treated and control groups, of which 59 were upregulated, and
   53 were downregulated. We selected 9 circRNAs for evaluation of
   specific head-to-tail splicing, and 10 differentially expressed
   circRNAs between the two groups for qRT-PCR validation. Gene Ontology
   and Kyoto Encyclopedia of Genes and Genomes analyses enrichment
   analyses revealed that the parental genes of the circRNAs mainly
   participated in cardiac myofibril assembly and positive regulation of
   DNA-templated transcription, indicating that they might be involved in
   cardiovascular diseases. Finally, we constructed a circRNA-miRNA
   network based on the dysregulated circRNAs and VSMC-related microRNAs.
   Our study is the first to show the differential expression of circRNAs
   in PDGF-BB-induced VSMCs and may provide new ideas and targets for the
   prevention and therapy of vascular diseases.

   Keywords: cardiovascular disease, RNA-seq, circRNA, VSMC, ceRNA

Introduction

   Atherosclerosis (AS) is the main pathological basis of cardiovascular
   and cerebrovascular diseases and is mediated by various factors. With
   socioeconomic development, the morbidity and mortality of
   cardiovascular diseases are increasing worldwide; consequently,
   cardiovascular disease has become one of the important diseases
   threatening public health, and its causes and pathomechanism are not
   yet clear ([41]Zhao et al., 2019). Vascular smooth muscle cells (VSMCs)
   are the major cellular components of the blood vessel wall, where they
   exist in a differentiated contractile phenotype to respond to arterial
   contraction and to produce extracellular matrix (ECM; [42]Basatemur et
   al., 2019). Accumulating evidence shows that abnormal VSMC
   proliferation and migration have an essential role in atherosclerotic
   lesion formation. Genetic lineage tracing studies have illustrated that
   in atherosclerotic plaques, especially progressing plaques, extensive
   lipids are released by damaged or dying macrophages, and VSMCs. Then,
   accumulating lipid infiltration appeared in the center of the plaque,
   forming the necrotic core. VSMCs migrate and proliferate to the
   surrounding of the necrotic core and play an important role in creating
   a fibrous cap that stabilizes the atherosclerotic plaque ([43]Feil et
   al., 2014; [44]Misra et al., 2018). Moreover, VSMCs can differentiate
   into many other cell types found in the plaque core, suggesting that
   these cells might participate in multiple processes underlying
   atherosclerotic plaque stability ([45]Tang et al., 2012;
   [46]Ackers-Johnson et al., 2015; [47]Durham et al., 2018; [48]Wang et
   al., 2019).

   An increasing body of evidence shows that the expression level of
   contractile SMC markers decreased is related with injury and
   inflammation, and which is associated with the reduced expression of
   MYOCD (a key factor regulating the contractile VSMC state in the
   development of plaques; [49]Ackers-Johnson et al., 2015). In vitro,
   studies have demonstrated that SMCs, stimulated by growth factors,
   oxidative stress, and inflammatory cytokines, can phenotypically switch
   into proliferating, and/or migrating cells. Among them,
   platelet-derived growth factor (PDGF-BB) is considered to be one of the
   most effective mitogens in the proliferation and migration of VSMC,
   which can initiate various biological effects by activating
   intracellular signal transduction pathways and play a significant role
   in regulating the proliferation and migration of VSMC ([50]Heldin and
   Westermark, 1999; [51]Dzau et al., 2002; [52]Shawky and Segar, 2017).
   Consequently, it will be necessary to find a new target to inhibit
   PDGF-mediated VSMC proliferation and migration which will exert an
   important therapeutic intervention in AS development.

   Non-coding RNAs (ncRNAs) are a group of biomolecules acting as pivotal
   regulators that play powerful and diverse roles in pathological and
   physiological processes ([53]de Almeida et al., 2016). Their gene
   expression patterns can also reveal changes in biological pathways that
   correlate with disease progression or even the risk of disease
   progression ([54]Bayoumi et al., 2016; [55]Zhang et al., 2019).
   Circular RNAs (circRNAs) are an emerging group of ncRNAs that are
   ubiquitous, stable, and evolutionarily conserved in eukaryotes
   ([56]Memczak et al., 2013). Though the phenomenon of RNA cyclization
   was first reported in the 1970s ([57]Sanger et al., 1976), circRNAs
   were considered as byproducts of aberrant splicing during transcription
   and remained underappreciated. As RNA sequencing technologies evolve,
   accompanied by the development of computational algorithms, numerous
   circRNAs have been discovered ([58]Salzman et al., 2012). Notably,
   circRNAs have been found extensively in different species and are
   closely related to various diseases, including cardiopathy, which has a
   great impact on human health ([59]Burd et al., 2010; [60]Holdt et al.,
   2016; [61]Huang et al., 2019). ncRNAs, especially microRNAs (miRNAs),
   and circRNAs, can function as competitive endogenous RNAs (ceRNAs)
   which can construt gene regulatory networks to regulate the expression
   of multiple genes with spatiotemporal specificity. Given the
   characteristics of ncRNAs, they could have great potential application
   in the treatment of diseases. However, the circRNA expression profiles
   and whether circRNAs participate in the regulatory of VSMCs still
   remain not clear. In the present study, we aimed to perform
   high-throughput RNA sequencing in paired PDGF-BB-treated VSMCs (PS) and
   a normal control group (NC) to investigate VSMC-specific circRNA
   profiles, as well as potential functional characterization of the
   representative candidate circRNAs. Our study is the first to show the
   differential expression of circRNAs in PDGF-BB-induced VSMCs and may
   provide new ideas and targets for the prevention and therapy of
   vascular diseases.

Materials and Methods

Cell Cultures

   Primary human aortic smooth muscle cells (HASMCs; ScienCell, United
   States) were cultured with Smooth Muscle Cell Medium (SMCM; ScienCell,
   United States) in a humidified incubator with 5% CO[2] at 37°C. HASMCs
   were inoculated at a density of 3 × 10^5 cells/well in 6-well culture
   plates overnight. Before the subsequent experiments, cells were made
   quiescent by starvation for 24 h and then treated with 10 ng/ml
   platelet-derived growth factor (PDGF-BB; Sigma-Aldrich) for 48 h. The
   blank control group did not receive PDGF-BB. Each group had three
   samples.

Immunofluorescent Staining

   Human aortic smooth muscle cells treated with PDGF-BB and the contol
   were stained for a-SMA (abcam, United States). Brifly, cells of two
   groups were fixed with 4% paraformaldehyde for 20 min or overnight at
   4°C, then, washed for 20 min in 0.5% Triton X-100 (Solarbio, China).
   Afterward, cells were blocked in 2% BSA for 30 min, then, washed in
   phosphate-buffered saline (PBS) for twice/10 min each time. Primary
   antibodies (a-SMA, 1:400 dilution) were added over night at 4°C, rinsed
   3 times/10 min each time in PBS the following day, and subsequently the
   secondary antibody (1:1000 dilution) incubate at 37°C for 1 h. After
   incubation, the cells were washed 3 times with PBS, for 10 min each.
   Cell nucleus were labeled using DAPI for 5 min, then washed 3 times (5
   min each) in PBS. Finally, cells were imaged with a fluorescent
   microscope.

RNA Library Construction and Sequencing

   According to manufacturer’s instructions, total RNA was isolated from
   each sample using TRIzol reagent (Invitrogen, Carlsbad, CA, United
   States). The amount and purity of the total RNA were quantified using
   NanoDrop ND-1000 (NanoDrop, Wilmington, DE, United States), and the
   integrity of total RNA was assessed by Agilent 2100 with RIN number
   >7.0. Then circRNA library was constructed. Firstly, approximately 5 μg
   RNA was subjected to ribosomal RNA depletion with the Ribo-Zero^TM rRNA
   Removal Kit (Illumina, San Diego, CA, United States). Secondly, linear
   RNAs were removed with RNase R (Epicentre Inc., Madison, WI, United
   States) to enrich circRNAs (3U RNase R for per μg RNA). Finally, the
   RNA fragmentation was obtained using divalent cations under high
   temperature for reverse-transcribed to generate first-stranded cDNA,
   then, the second-stranded DNAs were next synthesized with Escherichia
   coli DNA polymerase I, RNase H, and dUTP. To constrcut strand-specific
   cDNA, we added specificity terminal amino modification of the DNA
   fragment ends to prepare them for ligation to the adapters. After
   amplified by Polymerase chain reaction (PCR), the library was purified
   and the average insert size was 300 bp (± 50 bp). Finally, paired-end
   were sequenced on an Illumina HiSeq 4000 (LC Bio, China) according to
   the recommended protocol. The sequencing data used and/or analyzed
   during the current study are available in NCBI databases. (BioProject
   [62]PRJNA607375).^[63]1

Bioinformatics Analysis

   Low-quality and undetermined bases was removed and sequence quality was
   verified using FastQC^[64]2. Then, we used Bowtie2 and Tophat2 to map
   reads to the reference genome. CIRCExplorer and TopHat-fusion were
   utilized for de novo assembly of the mapped reads to circRNA and
   recognizing back splicing reads in unmapped reads. All samples
   generated unique circRNA. The differentially expressed circRNAs with
   log2 (fold change) >1 or log2 (fold change) <−1 and with statistical
   significance (p value <0.05) by R package–edgeR were selected for
   further studies.

CircRNA Validation by PCR

   Polymerase chain reaction was used to validate the reliability of the
   high-throughput RNA sequencing data. A Transcriptor First Strand cDNA
   Synthesis Kit (Roche, Germany) was used for reverse transcription of
   circRNAs. According to manufacturer’s instructions, appropriate volume
   of master mix as well as RNA sample were prepared, then the reaction
   for reverse transcription was initiated at 25°C for 10 min, 55°C for 30
   min, and 85°C for 5 min. Then, cDNA and gDNA templates were PCR
   amplified for 35 cycles using Taq PCR MasterMix (Tiangen, China)
   following the manufacturer’s protocol, and PCR products were visualized
   using 2% GelRed-stained agarose gel. To confirm the PCR results, we
   further performed Sanger sequencing to directly examine the PCR
   product. To verify the accuracy of the differential expression of
   circRNAs, qRT-PCR was conducted using a FastStart Universal SYBR Green
   Master Kit (Roche, Germany). Briefly, the first strand cDNA was
   synthesized using random hexamer primer and then amplified by SYBR
   Green Kit following the standard procedure that is denaturation 95°C
   (10 min) followed by amplification by a total of 40 cycles of 95°C (15
   s) and 60°C (1 min) on an ABI7500 system (Applied Biosystems, Foster
   City, CA, United States). GAPDH was used as an internal control, and
   PCR primers are listed in [65]Supplementary Table S1.

GO and KEGG Pathway Analyses

   The differentially expressed circRNA-host gene data were analyzed by
   the DAVID tool (V6.8; [66]Huang da et al., 2009) with its GO function
   enrichment and KEGG pathway analyses. An enrichment gene count ≥2 and
   hypergeometric test significance threshold P value <0.05 were
   considered to indicate significant enrichment.

Interaction Between CircRNA and miRNA

   Vascular smooth muscle cell-associated miRNAs were selected from
   disease-miRNA interactions validated in previous studies ([67]Leeper
   and Maegdefessel, 2018; [68]Wang and Atanasov, 2019). For the obtained
   VSMC-related miRNAs, we predicted whether there was a regulatory
   relationship between them and the selected differentially expressed
   circRNAs. We used miRanda and TargetScan to predict the relationships
   between the VSMC-related miRNAs and the differentially expressed
   circRNAs, and the Cytoscape tool was used to construct a network map of
   target miRNAs and circRNAs.

Statistical Analysis

   Data were analyzed and visualized with SPSS 22.0 (IBM Corporation,
   Somers, NY, United States) and GraphPad Prism 5.0 (GraphPad Software,
   La Jolla, CA, United States). Data are presented as the mean ± standard
   deviation. Wilcoxon rank-sum test, Student’s t-test and fold change
   were used to analyze the significant differences between the sequencing
   data of samples. A t test was applied to compare qRT-PCR results.
   Differences with p <0.05 were considered statistically significant.

Results

CircRNA Expression Profiles in PDGF-BB-Treated VSMCs

   Cells were treated with 10 ng/ml PDGF-BB (PS), and the blank control
   group did not receive PDGF-BB (NC). After stimulation for 48 h,
   morphological changes and the expression levels of SM22α and α-SMA in
   the two groups were detected and are shown in [69]Supplementary Figures
   S1A–C. As expected, VSMCs tended to phenotypically switch to function
   as proliferative and/or migratory cells in response to stimulation by
   PDGF-BB ([70]Heldin and Westermark, 1999; [71]Allahverdian et al.,
   2018). Compared to the NC, the cell morphology became spindle-shaped
   and elongated, and the expression of differentiation-associated genes
   and proteins was decreased in the PS, suggesting that the cells had a
   stronger capacity for proliferation. In the meantime, 7 known SMC
   markers were detected in NC and the PS group and we added a heatmap of
   the differential mRNA expression between the two groups
   ([72]Supplementary Figure S1D). The result showed that the synthesis
   markers of KRT8 and TLR4 were significantly increased and contractile
   markers including MYH11, SMTN, CNN1, SMM22α, and α-SMA were reduced in
   PDGF-BB treated HASMC, which corresponded to the privious study. These
   studies fully confirmed that the cells gain a proliferative phenotype
   after treatment with PDGF-BB.

   To investigate the effects of PDGF-BB on circRNA expression in VSMCs,
   two groups of HASMCs were prepared for high-throughput sequencing using
   an Illumina HiSeq 4000 (LC Bio, China). We used CIRCExplorer to de novo
   assembly of the mapped reads and to identify back splicing reads. The
   following criteria were restricted for circRNA identification: (1)
   mismatch ≤2; (2) back-spliced junction reads ≥1; and (3) two splice
   sites <100 kb apart on the genome. Accordingly, a total of 22191
   circRNAs, corresponding to 6273 genes (host genes) in the PS, NC, or
   both groups, were detected, of which 7322 and 7870 circRNAs were
   specifically expressed in the NC and PS groups, respectively. A total
   of 6999 circRNAs were identified in both the PS and NC groups
   ([73]Figure 1A). Further analysis revealed that three categories of
   circRNAs were represented: exons (94.06%), introns (5.43%), and
   intergenic regions (0.50%; [74]Figure 1B). The results showed that the
   majority of the circRNAs originated from protein-coding exons. By
   analyzing the sequencing data, we identified 5794 circRNAs recorded in
   circBase^[75]3 and 16397 novel circRNAs that were discovered in this
   study. The chromosomal distribution of all circRNAs showed that these
   circRNAs were distributed on almost all human chromosomes ([76]Figure
   1C).

FIGURE 1.

   [77]FIGURE 1
   [78]Open in a new tab

   Overview of the identified circular RNAs (circRNAs) in PDGF-BB-treated
   VSMCs (PS) and controls (NC). (A) The Venn diagram shows the number of
   unique and common circRNAs in PS and NC. (B) The pie chart displays the
   ratio of circRNAs originating from exonic, intronic, and intergenic
   regions. (C) Chromosomal distribution of all identified circRNAs.

   By the criteria of log2 (fold change) >1 or log2 (fold change) <−1 and
   p value <0.05, we identified 112 differentially expressed circRNAs
   between the PS and NC groups, of which 59 were upregulated, and 53 were
   downregulated. The DE circRNAs in the samples of the two groups are
   displayed with a Volcano plot, bar graph, and heatmap. Additionally,
   the chromosome distributions of the DE circRNAs are shown in the bar
   graph ([79]Figures 2A–D). The top ten upregulated and downregulated
   circRNAs are listed in [80]Table 1.

FIGURE 2.

   [81]FIGURE 2
   [82]Open in a new tab

   Differentially expressed (DE) circRNAs in PS and NC. (A) and (B)
   display the volcano plot and bar graph for the DE circRNAs in the
   samples of the two groups. (C) The chromosome distributions of
   differentially expressed circRNAs. (D) The DE circRNAs and samples are
   coclustered by hierarchical clustering analysis. The upper maps are
   based on DE circRNAs with log2 (fold change) >1 or log2 (fold change)
   <-1 and p value <0.05 for the comparisons of PS vs NC.

TABLE 1.

   Top ten upregulated and downregulated circRNAs.
   CircRNA Name Gene symbol Regulation Annotation Chromosome Fold change
   (log2) P value
   CircRNA12134 [83]AC004922.1 Up Exon Chr7 5.312 0.011
   CircRNA6181 SSH1 Up Exon Chr12 4.847 0.007
   CircRNA7019 AP1G1 Up Exon Chr16 4.691 0.013
   CircRNA7000 SNTB2 Up Exon Chr16 4.646 0.016
   CircRNA8379 HMGCR Up Exon Chr5 4.605 0.016
   CiRNA10248 CDR1 Up Exon ChrX 4.509 0.014
   CircRNA10737 HINFP Up Exon Chr11 3.905 0.016
   CircRNA13693 SOX13 Up Exon Chr1 3.238 0.028
   CircRNA13360 GIGYF2 Up Exon Chr2 3.222 0.030
   CircRNA8979 HPCAL1 Up Exon Chr2 3.111 0.028
   CircRNA5297 TBCE Down Exon Chr1 –5.159 0.049
   CircRNA876 CCNY Down Exon Chr10 –4.855 0.000
   CircRNA198 PCCA Down Exon Chr13 –4.714 0.049
   CircRNA5218 NEK7 Down Exon Chr1 –4.629 0.039
   CircRNA3568 PRELID2 Down Exon Chr5 –4.602 0.019
   CircRNA3679 AFAP1 Down Exon Chr4 –4.283 0.033
   CircRNA1611 IST1 Down Exon Chr16 –4.270 0.009
   CiRNA46 SRF Down Intron Chr6 –4.130 0.004
   CircRNA4487 MITD1 Down Exon Chr2 –4.052 0.008
   CircRNA793 ZMYND11 Down Exon Chr10 –4.046 0.015
   [84]Open in a new tab

Validation of VSMC-Enriched CircRNAs

   To verify the accuracy of the RNA-seq data, 9 circRNAs (circRNA2637,
   circRNA4624, circRNA4487, circRNA3875, circRNA4209, circRNA5591,
   circRNA5550, circRNA5497, and circRNA5223) were randomly selected for
   validation experiments. We used PCR to evaluate specific head-to-tail
   splicing. First, divergent (circular) and convergent (linear)
   circRNA-specific primers were designed for RT-PCR to verify that the
   selected candidate RNAs are indeed circRNAs. The results showed that
   the divergent primers produced amplicons from RNA-derived samples and
   not from genomic DNA. The PCR products were visualized using 2%
   GelRed-stained agarose gel ([85]Figure 3A). Then, Sanger sequencing was
   performed to validate the PCR product, and the head-to-tail splice
   junctions were identified, unambiguously demonstrating that the
   selected candidates were circRNAs ([86]Figure 3B). Finally, ten
   circRNAs (threshold: a fold change greater than 1 and a p value less
   than 0.05 in two comparisons that were differentially expressed in the
   two groups) were selected for qRT-PCR validation, and three biological
   replicates were performed. The results showed that the expression
   levels of circRNA-4452, circRNA-13360, circRNA-1698, circRNA-8979, and
   circRNA-14411 were significantly upregulated, and the expression levels
   of circRNA-3041, circRNA-5780, circRNA-1848, and circRNA-3875 were
   significantly downregulated. Accordingly, Three of the candidate
   circRNAs were identified the sequence of head-to-tail splice junctions
   directly by Sanger sequencing of PCR amplicons ([87]Supplementary
   Figure S2). The qRT-PCR assay results were consistent with our RNA-seq
   assay results, confirming the accuracy of sequencing. However,
   circRNA-536 was not consistently and/or significantly differentially
   expressed between the two groups ([88]Figures 3C,D).

FIGURE 3.

   [89]FIGURE 3
   [90]Open in a new tab

   Validation of VSMC-enriched circRNAs. (A) Nine circRNAs were selected
   for validation experiments. RT-PCR with divergent (circular) and
   convergent (linear) primers was used to confirm the candidate circRNAs.
   Divergent (circular) primers (◀▶) successfully amplified a single
   fragment at the expected sizes from cDNA but not from genomic DNA
   (gDNA). Convergent (linear) primers (▶◀) could amplify from both cDNA
   and gDNA. (B) Sanger sequencing of the selected candidate circRNAs
   shows the back-splice junctions. (C,D) The relative expression levels
   of 10 DE circRNAs were determine by qRT-PCR. The data are presented as
   the mean ± SD, n = 3. *P <0.05, **P <0.01, and ***P <0.001.

GO and KEGG Analyses of the Dysregulated CircRNA Parental Genes

   Previous studies have demonstrated that circRNAs are closely related to
   their parental genes and have the ability to regulate their parental
   genes ([91]Zhang et al., 2016; [92]Wei et al., 2017). Thus, to further
   investigate the panorama of circRNA functions and interactions in
   VSMCs, we performed GO function enrichment analysis and KEGG pathway
   enrichment analysis based on the significantly differentially expressed
   circRNA host genes. The results of GO enrichment analysis showed that
   782 GO BP (biological process), 198 GO CC (cellular component), and 241
   GO MF (molecular function) terms were enriched. The top 25 (GO BP), top
   15 (GO CC), and top 10 (GO MF) are displayed in [93]Figure 4A. GO
   enrichment showed that 66 genes were enriched in protein binding, 53
   genes were expressed in the cytoplasm, and 16 genes were mainly
   associated with regulation of transcription, DNA-templated. In the GO
   category “cellular component,” the most significant terms were actin
   cytoskeleton, clathrin-coated vesicle and flotillin complex, while in
   the GO category “molecular function,” the main molecular functions were
   actin binding, clathrin heavy chain binding, coenzyme binding,
   transcription factor activity, and RNA polymerase II transcription
   factor binding. In the GO category “biological process,” neural crest
   cell migration, positive regulation of transcription via serum response
   element binding, cardiac myofibril assembly, and positive regulation of
   DNA-templated transcription were the most representative significant
   terms. The top 20 GO terms are displayed in a scatter plot ([94]Figure
   4B). KEGG pathway enrichment analysis suggested that there were 81
   signaling pathways related to the differentially expressed genes, among
   which the “RNA degradation,” and the “phototransduction” signaling
   pathways were the most significant pathways. The top 20 pathways are
   shown in [95]Figure 4C. The two most significant pathways are displayed
   in [96]Figure 5.

FIGURE 4.

   [97]FIGURE 4
   [98]Open in a new tab

   GO and KEGG pathway analysis of the parental genes of the identified
   circRNAs. (A) The top 25 GO BP, top 15 GO CC, and top 10 GO MF are
   displayed. (B) The top 20 most represented significant GO terms are
   displayed in a scatter plot. (C) The top 20 enriched KEGG pathways of
   the circRNA parental genes.

FIGURE 5.

   [99]FIGURE 5
   [100]Open in a new tab

   Genes mapped to KEGG pathways by pathway analysis. (A) and (B) show the
   signaling pathways “RNA degradation” and “phototransduction,” which
   were the most significant pathways.

Construction of a CircRNA and Disease-Related miRNA Network

   To further explore the regulatory mechanism of the DE circRNAs and
   investigate the relationships between DE circRNAs and the function of
   VSMCs, we compared VSMC-associated miRNAs in validated
   disease-associated miRNA. A detailed list of the miRNAs is provided in
   [101]Table 2. Based on the ceRNA regulatory mechanism, TargetScan, and
   miRanda software were used to analyze the miRNAs binding sites for DE
   circRNA. Finally, we constructed a circRNA-miRNA interaction network,
   73 nodes (23 DE circRNAs and 50 miRNAs) and 83 edges were identified in
   the circRNA–miRNA network ([102]Figure 6). According to the network, we
   predicted that 12 down- and 11 upregulated circRNAs have miRNA binding
   sites that could act as ceRNAs participating in the regulation of
   posttranscriptional gene expression. Notably, we found that circRNA160
   (hsa_circ_0008776) showed the highest degree of connectivity in the
   network, with up to 11 miRNA binding sites, followed by circRNA13360
   (hsa_circ_0003341), and circRNA7637 (hsa_circ_0001222), indicating
   their potential important function in VSMCs and cardiovascular disease.

TABLE 2.

   Detailed list of the VSMC-associated miRNAs.
   miRNA Target(s) Role and function in SMC dynamics References