Skip to main content
NCBI home page
Frontiers in Cardiovascular Medicine logoLink to Frontiers in Cardiovascular Medicine
. 2022 Jul 7;9:850991. doi: 10.3389/fcvm.2022.850991

Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction

Jiahe Wu 1,2,†, Chenze Li 1,2,†, Zhe Lei 1,2,†, Huanhuan Cai 1,2, Yushuang Hu 1,2, Yanfang Zhu 1,2, Tong Zhang 1,2, Haoyan Zhu 1,2, Jianlei Cao 1,2,*,‡, Xiaorong Hu 1,2,*,‡
PMCID: PMC9300925  PMID: 35872921

Abstract

Background

Circular RNA (circRNA) plays an important role in the regulation of gene expression and the occurrence of human diseases. However, studies on the role of circRNA in acute myocardial infarction (AMI) are limited. This study was performed to explore novel circRNA-related regulatory networks in AMI, aiming to better understand the molecular mechanism of circRNAs involvement in AMI and provide basis for further scientific research and clinical decision-making.

Methods

The AMI-related microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. After differential expression analysis, the regulatory relationships between these DERNAs were identified by online databases circBank, circInteractome, miRDB, miRWalk, Targetscan, and then two circRNA-miRNA-mRNA regulatory networks were constructed. Differentially expressed genes (DEGs) in this network were selected followed by enrichment analysis and protein–protein interaction (PPI) analysis. Hub genes were identified using Cytohubba plug-in of Cytoscape software. Hub genes and hub gene-related miRNAs were used for receiver operating characteristic curve (ROC) analysis to identify potential biomarkers. The relative expression levels of these biomarkers were further assessed by GSE31568 (miRNA) and GSE66360 (mRNA). Finally, on the basis of the above analysis, myocardial hypoxia model was constructed to verify the expression of Hub genes and related circRNAs.

Results

A total of 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs were significantly differentially expressed in these datasets. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. These circRNA-related DEGs were mainly enriched in the terms of “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” The top 10 hub genes ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified from the PPI network. Hub genes GPM6A, SRSF1, ANK2 and hub gene-related circRNAs hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, and hsa_circ_0045519 were identified as potential biomarkers in AMI.

Conclusion

In this study, the potential circRNAs associated with AMI were identified and two circRNA-miRNA-mRNA regulatory networks were constructed. This study explored the mechanism of circRNA involvement in AMI and provided new clues for the selection of new diagnostic markers and therapeutic targets for AMI.

Keywords: acute myocardial infarction, circRNA, bioinformatics analysis, biomarker, circRNA-miRNA-mRNA regulatory network

Introduction

Cardiovascular disease is a serious threat to human health. Ischemic heart disease remains one of the leading causes of death and disability worldwide (1). The high incidence and mortality of acute myocardial infarction (AMI) causes a serious social and health economic burden and affects the quality of human life.

Acute myocardial infarction is the result of myocardial ischemic necrosis caused by acute interruption of myocardial blood flow, often characterized by acute thrombosis superimposed on atherosclerotic plaque rupture (2). Early successful reperfusion therapy [thrombolytic therapy or percutaneous coronary intervention (PCI)] is the best way to reduce the size of myocardial infarction and improve the clinical outcome (3). Although the detection of higher sensitivity troponin improved the recognition of AMI patients suitable for PCI, it still had some defects in the recognition of early infarction and mild cardiac injury (4). Recently, non-coding RNAs (ncRNA), including circular RNAs (circRNA), long non-coding RNAs (lncRNA), microRNAs (miRNAs), as new biological markers of AMI, has attracted the attention of scientists.

Circular RNAs, characterized by a covalently closed continuous loop, is a type of circular single-stranded RNA different from other linear RNAs (5). They are produced by reverse splicing of the precursor mRNA transcript, in which the upstream splicing receptor connects to the downstream splicing donor (6). As an effective sponge of miRNA, circRNA interacts with miRNA to regulate mRNA expression (7). A large number of studies have demonstrated that circRNA is involved in the development of a variety of cardiovascular diseases, including heart failure (8), atrial fibrillation (9), diabetic cardiomyopathy (10), dilated cardiomyopathy (11), acute myocardial infarction (12), and myocardial ischemia-reperfusion injury (13). A study reported that circRNA SNRK acts as a sponge for miR-103-3p and regulates myocardial apoptosis through GSK3β/β-catenin pathway. Overexpression of circRNA SNRK can significantly reduce myocardial cell apoptosis and promote cardiac repair in myocardial infarction rats (14). Another study showed that circRNA Ube3a derived from M2 macrophages can promote proliferation, migration and phenotypic transformation of cardiac fibroblasts (CFs) cells by targeting miR-138-5p/RHOC axis, aggravating cardiac fibrosis after AMI (15). In addition, circ_0023461 silencing protects cardiomyocytes from hypoxia-induced dysfunction by targeting miR-370-3p/PDE4D axis (16). These findings suggest that circRNA is involved in the pathophysiological process of AMI and plays an important regulatory role in the injury and repair of infarcted myocardium. Therefore, circRNA may be used as a biomarker of myocardial infarction.

This study was performed to construct the circRNA-miRNA-mRNA regulatory Network and identify novel potential biomarkers in AMI. Datasets GSE160717, GSE31568, GSE61741, GSE24519, and GSE66360 were downloaded and then analyzed using Bioinformatics analysis. Our study would provide new insights of molecular mechanisms of circRNAs involved AMI, aiming to understand the development and progression of AMI and find targets for its diagnosis and treatment. The workflow of the specific analysis is shown in Figure 1.

FIGURE 1.

FIGURE 1

Open in a new tab

Flowchart of the steps performed in this study.

Materials and Methods

Data Resource

The microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), GSE24519 (mRNA), and GSE66360 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database.1 GSE160717 is based on the GPL21825 platform (074301 Arraystar Human CircRNA microarray V2). GSE31568 and GSE61741 are based on the GPL9040 platform (febit Homo Sapiens miRBase 13.0). GSE24519 is based on the GPL2895 platform (GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray). GSE66360 is based on the GPL2895 platform ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). These datasets satisfy the following characteristics: (1) Organism: Homo sapiens; (2) Sample source: Blood; and (3) Grouping: AMI group and healthy controls. Table 1 shows the detailed information of these five microarray datasets. These datasets are widely used for bioinformatics analyses to explore the molecular mechanisms associated with AMI (17–19).

TABLE 1.

The detailed information of the 5 microarray datasets.

Data source Organism Platform Year Sample source Sample size (AMI/CON) Detected RNA type
GSE160717 Homo sapiens GPL21825 2020 Blood 3/3 circRNA
GSE31568 Homo sapiens GPL9040 2011 Blood 20/70 miRNA
GSE61741 Homo sapiens GPL9040 2014 Blood 62/94 miRNA
GSE24519 Homo sapiens GPL2895 2017 Blood 17/4 mRNA
GSE66360 Homo sapiens GPL570 2015 Blood 49/50 mRNA
Open in a new tab

Differential Expression Analysis

Raw sequencing data of GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were downloaded from the abovementioned platform and preliminary differential expression analysis between AMI samples and control was performed using GEO2R.2 Benjamini-hochberg method was used to correct adj. P for potential false positive results. FDR < 0.05 and |logFC| > 1.0 was set as the cut-off criteria of DERNAs. CoDEmiRNAs in GSE31568 and GSE61741were identified using Venn diagram web tool.3 Based on relative expression information in raw sequencing data, the heatmap of DEcircRNAs were produced using the package gg plots of R software (version: x64 3.2.1) (20).

circRNA and miRNA ID Conversion

Using circBase database,4 the circRNA name in GSE160717 containing six digits was converted into circBase ID containing seven digits. circRNAs that do not match in the annotation file were further blatted to complete the ID conversion. The miRDB5 database was used to convert the miRNA ID in GSE31568 and GSE61741 from the previous version to the latest version.

DEcircRNA Annotation and Structure Prediction

CircBase is a circRNA-related database that records circRNA-related annotation information, such as sequence, gene and genomic location (21). DEcircRNAs in GSE160717 were entered into the circBase database to get their annotation information. CSCD6 database is the first comprehensive cancer-specific circRNA database which can predict the microRNA response element (MRE) sites, RNA binding protein (RBP) sites, and potential open reading frames (ORF) (22). Structure of these DEcircRNAs were predicted and visualized using CSCD.

Construction of the circRNA-miRNA-mRNA Regulatory Network

Based on DEcircRNAs and CoDEmiRNAs, the circRNA-miRNA interactions were predicted using website tools of circBank7 and circInteractome8 (23, 24). Based on CoDEmiRNAs and differentially expressed genes (DEGs), the miRNA–mRNA interactions were predicted using website tools of miRDB,9 Targetscan (version7.210), and miRWalk11 (25–28). According to these regulatory relationships, the up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. The network diagram wsa visualized by Cytoscape software (version 3.7.1).

Functional and Pathway Enrichment Analysis

The Database for Annotation, Visualization, and Integrated Discovery (DAVID; version6.812) was used to perform Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs in these two circRNA-miRNA-mRNA regulatory networks, respectively (29). P value < 0.05 was set as the threshold to identify statistically significant pathways.

Construction of the Protein–Protein Interaction Network and Module Analysis

In order to better understand the molecular mechanism of circRNA’s involvement in transcriptional regulation, a protein–protein interaction network (PPI network) of DEGs in the circRNA-miRNA-mRNA network was constructed. The String database (version10.013) was used to predict the interaction between proteins, and a PPI network was established by Cytoscape software (version 3.7.1) subsequently.

The scores of Maximal Clique Centrality (MCC) algorithm was set as the criteria (30, 31), and the top10 Hub genes with high connectivity in the PPI network was screened out using the Cytohubba plug-in of Cytoscape software. In addition, the MCODE plug-in of Cytoscape software was used to predict meaningful modules with default parameters as thresholds. Finally, the key pathways of these modules were predicted by KOBAS (version 3.014) database.

Construction of the circRNA-miRNA-Hub Gene Regulatory Network

Based on hub genes identified in PPI network, corresponding miRNAs and circRNAs were selected from circRNA-miRNA-mRNA regulatory networks mentioned above. R software ggplot2, ggalluvial, and dplyr package was used to draw sankey diagrams (32). The heat map of circRNAs in this sankey diagram were produced using the package gg plots of R software (version: x64 3.2.1).

Identification and Validation of Potential Biomarkers

Hub gene and hub gene-related miRNAs in the sankey diagram were considered as candidate biomarkers for AMI. Based on their raw expression data in datasets GSE31568 (miRNA), GSE61741 (miRNA), GSE24519 (mRNA), and GSE66360 (mRNA), Graphpad Prism (version:8.0) software was used to plot the receiver operator characteristic curves and calculate the area under the curve (AUC) respectively to evaluate the predictive ability of these biomarkers for AMI. Meanwhile, hub genes and miRNAs with AUC ≥ 7.0 and P < 0.05 were screened as potential biomarkers. Their relative expression levels in these datasets were further evaluated by box plots. Graphpad Prism 9 software was used for analysis and mapping to compare the control and AMI groups. Comparison between the two groups was performed by independent sample T test. P < 0.05 was considered statistically significant.

Cell Culture and Treatments

Human cardiomyocyte AC16 cell line were purchased from BeNa Culture Collection (Beijing, China). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco) with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Sigma, St. Louis, MO, United States) at 37°C with 5% CO2.

The AC16 cardiomyocytes were exposed to 24 h of hypoxia (1% O2, 5% CO2, and 94% N2) in DMEM to generate a hypoxia model. AC16 cells under normoxia condition all the time were regarded as the control group.

Real-Time Quantitative Polymerase Chain Reaction

According to the manufacturer’s protocol, total RNA was isolated using FastPure® Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, Nanjing, China), and the mRNA and circRNA cDNAs were synthesized with Hifair® II 1st Strand cDNA Synthesis Kit (gDNA digester plus) (Ye Sen, Shanghai, China). The qRT-PCR reactions were carried out in the Bio-Rad CFX96 Real-time PCR Detection System, using Hieff UNICON® Universal Blue qPCR SYBR Green Master Mix (Ye Sen, Shanghai, China). GAPDH was used as the reference genes and the relative fold change was evaluated by the method of 2–ΔΔCt. All primers were presented in Supplementary File 3. Comparison between the two groups was performed by independent sample T test. P < 0.05 was considered statistically significant.

Results

Identification of Differentially Expressed Genes in Acute Myocardial Infarction

The microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. Based on GEO2R software analysis and screening criteria mentioned above, DERNAs in each dataset were identified. There were 83 DEcircRNAs, including 50 upregulated and 33 downregulated, in GSE160717. There were 249 DEmiRNAs, including 101 upregulated and 148 downregulated, in GSE31568. There were 143 DEmiRNAs, including 88 upregulated and 55 downregulated, in GSE61741. Finally, there were 1204 DEGs, including 766 upregulated and 438 downregulated, in GSE24519. Supplementary File 1 presents detailed results of differential expression analysis. The volcano plots of these DERNAs in each dataset are shown in Figures 2A–D. The online venn tool was used to identify CoDEmiRNAs in GSE31568 and GSE61741. Finally, 58 upregulated and 51 downregulated CoDEmiRNAs were found (Figures 2E,F). The top 5 high-expressed DERNAs and the top 5 low-expressed DERNAs in each dataset are shown in Table 2.

FIGURE 2.

FIGURE 2

Open in a new tab

Differential expression analysis. (A) The volcano plot of GSE160717. (B) The volcano plot of GSE31568. (C) The volcano plot of GSE61741. (D) The volcano plot of GSE24519. For panels (A–D), differentially expressed molecules were screened under the cut-off criteria | log2FC| > 1 and the FDR < 0.05. (E) Venn diagram of up-regulated DEmiRNAs in GSE31568 and GSE61741. (F) Venn diagram of down-regulated DEmiRNAs in GSE31568 and GSE61741.

TABLE 2.

The top 5 high-expressed DERNAs and the top 5 low-expressed DERNAs in each dataset.

Dataset Type DERNA Expression logFC FDR
GSE160717 circRNA hsa_circRNA_023461 Up 2.94 4.96E-02
GSE160717 circRNA hsa_circRNA_038011 Up 2.18 3.56E-02
GSE160717 circRNA hsa_circRNA_400564 Up 2.15 3.44E-02
GSE160717 circRNA hsa_circRNA_400027 Up 2.06 2.32E-02
GSE160717 circRNA hsa_circRNA_400101 Up 2.03 2.68E-02
GSE160717 circRNA hsa_circRNA_068655 Down –1.84 2.08E-02
GSE160717 circRNA hsa_circRNA_103560 Down –1.71 1.95E-02
GSE160717 circRNA hsa_circRNA_103110 Down –1.67 2.08E-02
GSE160717 circRNA hsa_circRNA_015677 Down –1.59 1.79E-02
GSE160717 circRNA hsa_circRNA_103111 Down –1.59 1.80E-02
GSE31568 miRNA hsa-miR-302b Up 3.64 2.19E-06
GSE31568 miRNA hsa-miR-1290 Up 3.62 1.34E-07
GSE31568 miRNA hsa-miR-508-3p Up 3.32 2.58E-05
GSE31568 miRNA hsa-miR-126* Up 3.29 5.43E-06
GSE31568 miRNA hsa-miR-1258 Up 3.07 1.22E-04
GSE31568 miRNA hsa-miR-1283 Down –4.10 8.15E-13
GSE31568 miRNA hsa-miR-519e* Down –3.11 3.21E-07
GSE31568 miRNA hsa-miR-518a-3p Down –2.97 3.21E-07
GSE31568 miRNA hsa-miR-1201 Down –2.97 5.84E-05
GSE31568 miRNA hsa-miR-31* Down –2.95 8.67E-08
GSE61741 miRNA hsa-miR-375 Up 2.35 2.25E-05
GSE61741 miRNA hsa-miR-142-3p Up 2.23 6.26E-05
GSE61741 miRNA hsa-miR-1258 Up 2.19 2.59E-05
GSE61741 miRNA hsa-miR-29c* Up 2.18 2.25E-05
GSE61741 miRNA hsa-miR-302b Up 2.10 1.12E-04
GSE61741 miRNA hsa-miR-1283 Down –1.88 6.99E-05
GSE61741 miRNA hsa-miR-31* Down –1.84 2.59E-05
GSE61741 miRNA hsa-miR-200a Down –1.80 6.79E-05
GSE61741 miRNA hsa-miR-1245 Down –1.66 1.65E-04
GSE61741 miRNA hsa-miR-155* Down –1.60 3.32E-04
GSE24519 mRNA ATXN7L1 Up 11.32 3.71E-08
GSE24519 mRNA PURA Up 10.78 7.69E-32
GSE24519 mRNA KRI1 Up 10.63 1.23E-09
GSE24519 mRNA C2orf48 Up 10.51 8.58E-06
GSE24519 mRNA HNRNPA2B1 Up 10.00 1.98E-13
GSE24519 mRNA LALBA Down –5.73 2.04E-06
GSE24519 mRNA TBX19 Down –5.14 1.24E-02
GSE24519 mRNA SRI Down –4.58 2.49E-04
GSE24519 mRNA P3H2 Down –4.57 2.93E-04
GSE24519 mRNA MARS2 Down –4.25 2.34E-05
Open in a new tab

DEcircRNA Annotation and Structure Prediction

The DEcircRNA’s name in GSE160717 containing six digits was converted into circBase ID containing seven digits (Supplementary File 2). Figure 3A is the Heatmap of these DEcircRNAs. DEcircRNAs were entered into the circBase database to get their annotation information. The annotation information of the top 5 high-expressed DEcircRNAs and the top 5 low-expressed DEcircRNAs are shown in Table 3. Structure of these DEcircRNAs were predicted and visualized using CSCD database. The structural patterns of the top 3 high-expressed DEcircRNAs and the top 3 low-expressed DEcircRNAs are shown in Figures 3B–G.

FIGURE 3.

FIGURE 3

Open in a new tab

DEcircRNAs in GES160717. (A) Heatmap of DEcircRNAs in GSE160717. (B) Structural pattern of hsa_circ_0023461. (C) Structural pattern of hsa_circ_0038011. (D) Structural pattern of hsa_circ_0018468. (E) Structural pattern of hsa_circ_0068655. (F) Structural pattern of hsa_circ_0001380. (G) Structural pattern of hsa_circ_0004771.

TABLE 3.

The annotation information of the top 5 high-expressed DEcircRNAs and the top 5 low-expressed DEcircRNAs.

circRNA name circRNA circBase ID Position Genomic length Strand Gene symbol Expression
hsa_circRNA_023461 hsa_circ_0023461 chr11:72406762-72423384 16622 – ARAP1 Up
hsa_circRNA_038011 hsa_circ_0038011 chr16:15120499-15123913 3414 + PDXDC1 Up
hsa_circRNA_400564 hsa_circ_0018468 chr10:65326097-65354614 28517 + REEP3 Up
hsa_circRNA_400027 hsa_circ_0092367 chr15:25325262-25326442 1180 + SNORD116-14 Up
hsa_circRNA_400101 hsa_circ_0092328 chr9:136216037-136216257 220 + SNORD24 Up
hsa_circRNA_068655 hsa_circ_0068655 chr3:196129822-196134264 4442 – UBXN7 Down
hsa_circRNA_103560 hsa_circ_0001380 chr3:196118683-196129890 11207 – UBXN7 Down
hsa_circRNA_103110 hsa_circ_0004771 chr21:16386664-16415895 29231 – NRIP1 Down
hsa_circRNA_015677 hsa_circ_0015677 chr1:185153374-185200840 47466 + SWT1 Down
hsa_circRNA_103111 hsa_circ_0061276 chr21:16415815-16415895 80 – NRIP1 Down
Open in a new tab

Construction of the circRNA-miRNA-mRNA Regulatory Network

In order to better understand the molecular mechanism of circRNAs involved in the process of AMI. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. Figure 4A is an “up-regulated circRNA”-“down-regulated miRNA”-“up-regulated mRNA” network, with 134 nodes and 166 edges, including 19 up-regulated circRNAs, 28 down-regulated miRNA and 87 up-regulated mRNAs. On the contrary, Figure 4B is a “down-regulated circRNA”-“up-regulated miRNA”-“down-regulated mRNA” network, with 72 nodes and 68 edges, including 18 down-regulated circRNAs, 15 up-regulated miRNA and 39 down-regulated mRNAs. These RNA interactions may provide new insight into the mechanism underlying AMI.

FIGURE 4.

FIGURE 4

Open in a new tab

circRNA-miRNA-mRNA regulatory Network of AMI. (A) Up-regulated circRNA corresponding circRNA-miRNA-mRNA regulatory Network. (B) Down-regulated circRNA corresponding circRNA-miRNA-mRNA regulatory Network. For panels (A,B), the orange-red regular hexagon represents up-regulated circRNA, the green regular hexagon represents down-regulated circRNA, the yellow diamond represents down-regulated miRNA, the purple diamond represents up-regulated miRNA, the blue oval represents up-regulated mRNA and the gray oval represents down-regulated mRNA.

Analysis of Enrichment of Genes in the circRNA-miRNA-mRNA Regulatory Network

Gene Ontology knowledgebase and KEGG database were used to characterize the functional roles of the DEGs in the circRNA-miRNA-mRNA Regulatory Network mentioned above. Figure 5 list the top10 enriched GO terms and KEGG pathways. The biological process (BP) category of the GO analysis results showed that these DEGs are significantly enriched in the term of “positive regulation of transcription, DNA-templated,” “negative regulation of apoptotic process,” and “calcium ion transmembrane transport,” etc. (Figure 5A). For GO cellular component (CC) analysis, the top 4 significantly enriched terms are “cytoplasm,” “neuron projection,” “neuronal cell body,” “actin cytoskeleton,” (Figure 5B). The top four significantly enriched molecular function (MF) terms include “RNA binding,” “enzyme binding,” “microtubule binding,” and “calcium channel activity” (Figure 5C). Furthermore, “FOXO signaling pathway,” “Carbohydrate digestion and absorption,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway” are pathways of significant enrichment in KEGG analysis (Figure 5D).

FIGURE 5.

FIGURE 5

Open in a new tab

Enrichment of genes in the circRNA-miRNA-mRNA Regulatory Network. (A) Bubble plot of biological process (BP; TOP10). (B) Bubble plot of cellular component (CC; TOP6). (C) Bubble plot of molecular function (MF; TOP5). (D) Circle plot of KEGG pathway enrichment analysis (TOP10).

Construction of the Protein-Protein Interaction Network and Identification of Hub Genes and Key Modules

The DEGs obtained from the circRNA-miRNA-mRNA Regulatory Network were introduced into the online database String. After removing the isolated genes without interaction, a PPI network with 105 nodes and 203 edges was established (Figure 6A). The 74 orange red nodes in the network represent up-regulated genes and the 31 blue nodes in the network represent down-regulated genes. Next, based on MCC method, the top 10 hub genes were filtered out using the plug-in Cytohubba in Cytoscape. They were ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1. The PPI network of these 10 hub genes is shown in Figure 6B. On the basis of MCODE, we identified 3 modules in the whole network (Figures 6C–E).

FIGURE 6.

FIGURE 6

Open in a new tab

Protein–Protein Interaction Network, Hub Genes and Key Modules. (A) The whole PPI Network. The 74 orange red nodes in the network represent up-regulated genes and the 31blue nodes in the network represent down-regulated genes. (B) PPI network of the 10 hub genes. (C) PPI network of module 1. (D) PPI network of module 2. (E) PPI network of module 3.

The KOBAS online tool was used for pathway enrichment analysis. The results indicated that “the DEGs in module 1” and “hub genes” are significantly enriched in the pathway of Calcium signaling pathway. The DEGs of module 2 are enriched in the pathway of RNA polymerase. In addition, the DEGs of module 3 are significantly enriched in the pathway of Peroxisome. Table 4 shows the top three significant modules and hub genes in the PPI network.

TABLE 4.

The top three significant modules and hub genes in the PPI network.

Plug-in Modules Nodes Edges Genes Pathway P-value
MCODE Module 1 7 19 ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A
CACNA1E, ANK2		 Calcium signaling pathway 1.01E-04
MCODE Module 2 3 3 CTPS1, RPAP3, POLR3A RNA polymerase 6.51E-03
MCODE Module 3 3 3 AMACR, HNRNPA2B1, PCSK5 Peroxisome 9.61E-03
CytoHubba Hub gene 10 23 ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A
CACNA1E, HDAC2, SRSF1, ANK2,HNRNPA2B1		 Calcium signaling pathway 5.45E-04
Open in a new tab

Hub Gene Related circRNAs AND miRNAs

Figure 7A is the sankey diagram of hub genes, hub gene related miRNAs and circRNAs selected from the circRNA-miRNA-mRNA regulatory network mentioned above. This sankey diagram contains 13 circRNAs,10 miRNAs and 10 hub genes, as well as 24 competing endogenous regulatory relationships. For example, hsa_circ_0023461 might act as a hsa-miR-589-5p sponge and play a regulatory role in acute myocardial infarction by influencing the expression of ATP2B2. In order to further analyze the relative expression of circRNA in this sankey diagram, heat map was used to further demonstrate (Figure 7B).

FIGURE 7.

FIGURE 7

Open in a new tab

Hub gene Related circRNAs and miRNAs. (A) The sankey diagram of the circRNA-miRNA-Hub gene network. (B) Heatmap of hub gene-related circRNAs.

Identification and Validation of Potential Biomarkers in Datasets

Receiver operating characteristic curve (ROC) analysis was performed on 10 miRNAs and 10 hub genes in the Sankey diagram and AUC values were calculated. Meanwhile, molecules with AUC ≥ 7 and P < 0.05 in two datasets were considered as potential biomarkers. Finally, hsa-miR-31-3p, hsa-miR-1291, hsa-miR-646, SRSF1 and HNRNPA2B1 were identified as potential markers of AMI. Figures 8A–E shows the detailed results of ROC analysis. GSE31568 (miRNA) and GSE66360 (mRNA) were used to further evaluate the relative expression of these molecules. As predicted by the previous analysis, hsa-miR-31-3p, hsa-miR-1291, hsa-miR-646, and SRSF1 were decreased, while HNRNPA2B1 was increased (Figures 8F–J).

FIGURE 8.

FIGURE 8

Open in a new tab

Potential biomarkers in datasets. (A) ROC curve of hsa-miR-31-3p in GSE31568. (B) ROC curve of hsa-miR-1291 in GSE31568. (C) ROC curve of hsa-miR-646 in GSE31568. (D) ROC curve of SRSF1 in GSE66360. (E) ROC curve of HNRNPA2B1 in GSE66360. (F) The relative expression of hsa-miR-31-3p in GSE31568. (G) The relative expression of hsa-miR-1291 in GSE31568. (H) The relative expression of hsa-miR-646 in GSE31568. (I) The relative expression of SRSF1 in GSE66360. (J) The relative expression of HNRNPA2B1 in GSE66360.

qRT-PCR Verification of Hub Genes and Related circRNAs

Hub genes (ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1) and Hub gene related circRNAs (hsa_circ_0023461, hsa_circ_0032623, hsa_circ_0030368, hsa_circ_0004561, hsa_circ_0047796, hsa_circ_0092367, hsa_circ_0025522, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, hsa_circ_0013599, hsa_circ_0045519, hsa_circ_0075503)were further verified in AC16 hypoxia modle. The results showed that 4 Hub genes and 8 Hub gene related circRNAs were differentially expressed. They were hsa_circ_0023461(up), hsa_circ_0030368 (down), hsa_circ_0004561 (up), hsa_circ_0001147 (down), hsa_circ_0004771(down), hsa_circ_0061276 (down), hsa_circ_0045519(up), hsa_circ_0075503(down), ATP2B2(down), GPM6A (down), SRSF1(down), and ANK2(up) (Figures 9A–L). The expression of hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, hsa_circ_0045519, GPM6A, SRSF1, and ANK2 werre consistent with the predicted result. Therefore, there may be hsa_circ_0001147/hsa-miR-204-5p/GPM6A axis, hsa_circ_0004771/hsa-miR-629-3p/SRSF1 axis, hsa_circ_0061276/hsa-miR-629-3p/SRSF1 axis, hsa_circ_0045519/hsa-miR-298/ANK2 axis, and hsa_circ_0004561 hsa-miR-298/ANK2 axis regulatory relationship involved in myocardial infarction.

FIGURE 9.

FIGURE 9

Open in a new tab

Expression of Hub gene and Hub gene related circRNAs in AC16 cell hypoxia model. For panels (A–L) *P < 0.05; **P < 0.01; ***P < 0.001.

Discussion

Acute myocardial infarction has a high incidence and mortality in the middle-aged and elderly worldwide. In the AMI-related research field, circRNA, as a key molecule for gene expression regulation, has attracted more and more attention from scientists in recent years. However, the available circRNA information of AMI is still very limited, and the selection of AMI-related key circRNAs is also a challenge. We use bioinformatics method to conduct data mining on AMI-related datasets, extract relevant biological information from high-dimensional data, and construct the corresponding circRNA-miRNA-mRNA regulatory Network, aiming to explore the mechanism of circRNA involvement in acute myocardial infarction. These identified circRNA and circRNA-related molecules may be used as biomarkers for AMI, providing basis for further scientific research and individualized diagnosis and treatment of clinical patients.

In the present study, we screened 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs in AMI-related datasets. Subsequently, the endogenous competition regulatory networks corresponding to the up-regulated and down-regulated circRNAs were constructed, respectively. GO and KEGG pathway enrichment analysis showed that DEGs in these two networks were significantly enriched in pathways related to cell cycle, apoptosis, autophagy and immune. These circRNA-related DEGs are involved in “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” These discovered pathways may be the key molecular mechanism for circRNAs participating in AMI. Based on PPI analysis, 10 hub genes, ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified. These genes are enriched in the pathway of Calcium signaling pathway. This indicated that circRNA may also participate in the regulation of calcium signaling pathway. In addition, 10 miRNAs and 13 circRNAs related to these 10 hub genes were screened out and hsa-miR-31-3p, hsa-miR-1291, hsa-miR-646, SRSF1 and HNRNPA2B1 were identified as potential markers of AMI.

CircRNA is stable and highly conserved among species, so it may be a key marker or therapeutic target for disease (33, 34). A diagnostic experimental study, including 1842 patients and 316 controls, showed that circ-YOD1 may act as a new biomarker for CAD (AUC = 0.824) (35). Our previous research also shows that CircSAMD4A can sponge miR-138-5p to promote H/R-induced inflammatory response and apoptosis (36). Therefore, circRNA may be involved in the process of AMI, and the circRNAs identified by us such as, hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276 and hsa_circ_0045519, etc, are likely to be potential markers or therapeutic targets of AMI. Further studies are needed to verify these circRNAs.

Some circRNA-related molecules such as hsa-miR-31-3p, hsa-miR-1291, hsa-miR-646, SRSF1 and HNRNPA2B1 have also been identified. One study has shown that miR-31 promotes poor cardiac remodeling and dysfunction in ischemic heart disease and therapeutic inhibition in vivo can improve cardiac insufficiency and prevent the development of poor remodeling (37). Another diagnostic experimental study, including 80 AMI patients and 80 controls, showed that miR-1291 may act as a new biomarker for AMI (38). SRSF1 (serine and arginine rich splicing factor 1) encodes a member of the arginine/serine-rich splicing factor protein family, which plays an important role in the regulation of gene expression in the immune system (39). HNRNPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1) is a kind of RNA binding protein, which complex with heterogeneous nuclear RNA (hnRNA) (40). One study showed that downregulation of HNRNPA2B1 reduced proliferation of vascular smooth muscle (41). As for the role of these molecules in AMI, further research is needed to prove them.

Further qPCR verification indicated the mechanism of circRNA participating in hypoxia-induced myocardial injury. There may be hsa_circ_0001147/hsa-miR-204-5p/GPM6A axis, hsa_circ_0004771/hsa-miR-629-3p/SRSF1 axis, hsa_circ_0061276/hsa-miR-629-3p/SRSF1 axis, hsa_circ_ 0045519/hsa-miR-298/ANK2 axis and hsa_circ_0004561 hsa-miR-298/ANK2 axis regulatory relationship involved in myocardial infarction. Further experimental verification is needed.

The above research results are comprehensive analysis of existing datasets. Therefore, these conclusions lack enough objective experimental evidence to support them, so they can only be the preliminary part of further research. In the future, we will carry out research work in large samples of people, and at the same time, we will further study the molecular mechanism of circRNA involved AMI in animal model.

Conclusion

In conclusion, we identified 83 DEcircRNAs and constructed the corresponding circRNA-miRNA-mRNA regulatory network. Bioinformatics analyses showed 43 miRNAs and 126DEGs were associated with these circRNA and they affect AMI through “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” CircRNA-related molecules such as hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, hsa_circ_0045519, GPM6A, SRSF1 and ANK2 may be potential biomarkers of AMI.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author Contributions

JW, CL, and ZL performed the data analysis and drafted the manuscript. HC, YH, and YZ prepared the figures and contributed toward the study design. TZ and HZ optimized the analysis protocol and participated in the data analysis. XH and JC revised the figures and designed and supervised the study. All authors have read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We sincerely appreciate the researchers for providing their GEO database information online, we are truly honored to acknowledge their contributions. We are also very grateful to sanger box online biomedical data analysis tool (http://sangerbox.com). It simplifies the analysis process.

Abbreviations

AMI

acute myocardial infarction

circRNA

circular RNA

lncRNA

long non-coding RNA

miRNA

microRNA

ncRNA

non-coding RNA

GEO

Gene Expression Omnibus

DERNA

Differential Expression RNA

PPI

protein–protein interaction

DEG

differentially expressed gene

PCI

percutaneous coronary intervention

CFs

cardiac fibroblasts

MRE

microRNA response element

RBP

RNA binding protein

ORF

open reading frames

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

BP

biological process

CC

cellular component

MF

molecular function

ROC

receiver operating characteristic curve.

Footnotes

Funding

This study was supported by a grant from the National Natural Science foundation of China (No. 81370308; XH) and the research fund from medical Sci-Tech innovation platform of Zhongnan Hospital, Wuhan University (No. ptxm2021009; JC).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2022.850991/full#supplementary-material

Click here for additional data file. (137.2KB, XLSX)
Click here for additional data file. (16.1KB, XLSX)
Click here for additional data file. (15.8KB, XLSX)

References

  • 1.Ramachandra C, Hernandez-Resendiz S, Crespo-Avilan GE, Lin YH, Hausenloy DJ. Mitochondria in acute myocardial infarction and cardioprotection. Ebiomedicine. (2020) 57:102884. 10.1016/j.ebiom.2020.102884 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Gossage JR. Acute myocardial infarction. Reperfusion strategies. Chest. (1994) 106:1851–66. 10.1378/chest.106.6.1851 [DOI] [PubMed] [Google Scholar]
  • 3.Yellon DM, Hausenloy DJ. Myocardial reperfusion injury. N Engl J Med. (2007) 357:1121–35. 10.1056/NEJMra071667 [DOI] [PubMed] [Google Scholar]
  • 4.Schulte C, Barwari T, Joshi A, Theofilatos K, Zampetaki A, Barallobre-Barreiro J, et al. Comparative analysis of circulating noncoding RNAs versus protein biomarkers in the detection of myocardial injury. Circ Res. (2019) 125:328–40. 10.1161/CIRCRESAHA.119.314937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Zeng X, Lin W, Guo M, Zou Q. A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol. (2017) 13:e1005420. 10.1371/journal.pcbi.1005420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Chen Z, Ren R, Wan D, Wang Y, Xue X, Jiang M, et al. Hsa_circ_101555 functions as a competing endogenous RNA of miR-597-5p to promote colorectal cancer progression. Oncogene. (2019) 38:6017–34. 10.1038/s41388-019-0857-8 [DOI] [PubMed] [Google Scholar]
  • 7.Yang W, Sun L, Cao X, Li L, Zhang X, Li J, et al. Detection of circRNA biomarker for acute myocardial infarction based on system biological analysis of RNA expression. Front Genet. (2021) 12:686116. 10.3389/fgene.2021.686116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Wang K, Long B, Liu F, Wang JX, Liu CY, Zhao B, et al. A circular RNA protects the heart from pathological hypertrophy and heart failure by targeting miR-223. Eur Heart J. (2016) 37:2602–11. 10.1093/eurheartj/ehv713 [DOI] [PubMed] [Google Scholar]
  • 9.Hu X, Chen L, Wu S, Xu K, Jiang W, Qin M, et al. Integrative analysis reveals key circular RNA in atrial fibrillation. Front Genet. (2019) 10:108. 10.3389/fgene.2019.00108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wang W, Zhang S, Xu L, Feng Y, Wu X, Zhang M, et al. Involvement of circHIPK3 in the pathogenesis of diabetic cardiomyopathy in mice. Diabetologia. (2021) 64:681–92. 10.1007/s00125-020-05353-8 [DOI] [PubMed] [Google Scholar]
  • 11.Lin Z, Zhao Y, Dai F, Su E, Li F, Yan Y. Analysis of changes in circular RNA expression and construction of ceRNA networks in human dilated cardiomyopathy. J Cell Mol Med. (2021) 25:2572–83. 10.1111/jcmm.16251 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chen TP, Zhang NJ, Wang HJ, Hu SG, Geng X. Knockdown of circROBO2 attenuates acute myocardial infarction through regulating the miR-1184/TRADD axis. Mol Med. (2021) 27:21. 10.1186/s10020-021-00275-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Zhou LY, Zhai M, Huang Y, Xu S, An T, Wang YH, et al. The circular RNA ACR attenuates myocardial ischemia/reperfusion injury by suppressing autophagy via modulation of the pink1/FAM65B pathway. Cell Death Differ. (2019) 26:1299–315. 10.1038/s41418-018-0206-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Zhu Y, Zhao P, Sun L, Lu Y, Zhu W, Zhang J, et al. Overexpression of circRNA SNRK targets miR-103-3p to reduce apoptosis and promote cardiac repair through GSK3beta/beta-catenin pathway in rats with myocardial infarction. Cell Death Discov. (2021) 7:84. 10.1038/s41420-021-00467-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Wang Y, Li C, Zhao R, Qiu Z, Shen C, Wang Z, et al. CircUbe3a from M2 macrophage-derived small extracellular vesicles mediates myocardial fibrosis after acute myocardial infarction. Theranostics. (2021) 11:6315–33. 10.7150/thno.52843 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ren K, Li B, Jiang L, Liu Z, Wu F, Zhang Y, et al. circ_0023461 silencing protects cardiomyocytes from hypoxia-induced dysfunction through targeting miR-370-3p/PDE4D signaling. Oxid Med Cell Longev. (2021) 2021:8379962. 10.1155/2021/8379962 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Zhao E, Xie H, Zhang Y. Predicting diagnostic gene biomarkers associated with immune infiltration in patients with acute myocardial infarction. Front Cardiovasc Med. (2020) 7:586871. 10.3389/fcvm.2020.586871 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Mujalli A, Banaganapalli B, Alrayes NM, Shaik NA, Elango R, Al-Aama JY. Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis. Genomics. (2020) 112:5072–85. 10.1016/j.ygeno.2020.09.004 [DOI] [PubMed] [Google Scholar]
  • 19.He J, Li X, Zhang Y, Zhang Q, Li L. Comprehensive analysis of ceRNA regulation network involved in the development of coronary artery disease. Biomed Res Int. (2021) 2021:6658115. 10.1155/2021/6658115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wu X, Sui Z, Zhang H, Wang Y, Yu Z. Integrated analysis of lncRNA-mediated ceRNA network in lung adenocarcinoma. Front Oncol. (2020) 10:554759. 10.3389/fonc.2020.554759 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Glazar P, Papavasileiou P, Rajewsky N. circBase: a database for circular RNAs. RNA. (2014) 20:1666–70. 10.1261/rna.043687.113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Xia S, Feng J, Chen K, Ma Y, Gong J, Cai F, et al. CSCD: a database for cancer-specific circular RNAs. Nucleic Acids Res. (2018) 46:D925–9. 10.1093/nar/gkx863 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Liu M, Wang Q, Shen J, Yang BB, Ding X. Circbank: a comprehensive database for circRNA with standard nomenclature. RNA Biol. (2019) 16:899–905. 10.1080/15476286.2019.1600395 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dudekula DB, Panda AC, Grammatikakis I, De S, Abdelmohsen K, Gorospe M. CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol. (2016) 13:34–42. 10.1080/15476286.2015.1128065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. (2015) 4:e05005. 10.7554/eLife.05005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. (2020) 48:D127–31. 10.1093/nar/gkz757 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: an online resource for prediction of microRNA binding sites. PLoS One. (2018) 13:e206239. 10.1371/journal.pone.0206239 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wu J, Cao J, Fan Y, Li C, Hu X. Comprehensive analysis of miRNA-mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse. BMC Med Genomics. (2021) 14:283. 10.1186/s12920-021-01134-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Dennis GJ, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. (2003) 4:3. [PubMed] [Google Scholar]
  • 30.Wan Y, Zhang X, Leng H, Yin W, Zeng W, Zhang C. Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis. PeerJ. (2020) 8:e9120. 10.7717/peerj.9120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Cao L, Chen Y, Zhang M, Xu D, Liu Y, Liu T, et al. Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis. PeerJ. (2018) 6:e5180. 10.7717/peerj.5180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Luo Q, Zhang X. Construction of protein-related risk score model in bladder urothelial carcinoma. Biomed Res Int. (2020) 2020:7147824. 10.1155/2020/7147824 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zhang HD, Jiang LH, Sun DW, Hou JC, Ji ZL. CircRNA: a novel type of biomarker for cancer. Breast Cancer Tokyo. (2018) 25:1–7. 10.1007/s12282-017-0793-9 [DOI] [PubMed] [Google Scholar]
  • 34.Zhao C, Liu J, Ge W, Li Z, Lv M, Feng Y. Identification of regulatory circRNAs involved in the pathogenesis of acute myocardial infarction. Front Genet. (2020) 11:626492. 10.3389/fgene.2020.626492 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Miao L, Yin RX, Zhang QH, Liao PJ, Wang Y, Nie RJ, et al. A novel circRNA-miRNA-mRNA network identifies circ-YOD1 as a biomarker for coronary artery disease. Sci Rep. (2019) 9:18314. 10.1038/s41598-019-54603-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Hu X, Ma R, Cao J, Du X, Cai X, Fan Y. CircSAMD4A aggravates H/R-induced cardiomyocyte apoptosis and inflammatory response by sponging miR-138-5p. J Cell Mol Med. (2020). 26:1776–84. 10.1111/jcmm.16093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Martinez EC, Lilyanna S, Wang P, Vardy LA, Jiang X, Armugam A, et al. MicroRNA-31 promotes adverse cardiac remodeling and dysfunction in ischemic heart disease. J Mol Cell Cardiol. (2017) 112:27–39. 10.1016/j.yjmcc.2017.08.013 [DOI] [PubMed] [Google Scholar]
  • 38.Chen L, Bai J, Liu J, Lu H, Zheng KA. Four-microRNA panel in peripheral blood identified as an early biomarker to diagnose acute myocardial infarction. Front Physiol. (2021) 12:669590. 10.3389/fphys.2021.669590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Paz S, Ritchie A, Mauer C, Caputi M. The RNA binding protein SRSF1 is a master switch of gene expression and regulation in the immune system. Cytokine Growth Factor Rev. (2021) 57:19–26. 10.1016/j.cytogfr.2020.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Moran-Jones K, Wayman L, Kennedy DD, Reddel RR, Sara S, Snee MJ, et al. hnRNP A2, a potential ssDNA/RNA molecular adapter at the telomere. Nucleic Acids Res. (2005) 33:486–96. 10.1093/nar/gki203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Zhang RY, Wu CM, Hu XM, Lin XM, Hua YN, Chen JJ, et al. LncRNA AC105942.1 downregulates hnRNPA2/B1 to attenuate vascular smooth muscle cells proliferation. DNA Cell Biol. (2021) 40:652–61. 10.1089/dna.2020.6451 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (137.2KB, XLSX)
Click here for additional data file. (16.1KB, XLSX)
Click here for additional data file. (15.8KB, XLSX)

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Articles from Frontiers in Cardiovascular Medicine are provided here courtesy of Frontiers Media SA

RESOURCES