Dataset Information

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

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.

SUBMITTER: Wu J

PROVIDER: S-EPMC9300925 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics. Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the "limma" package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The "clusterProfiler" package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells. Results: Using the "limma" package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.

Project description:BackgroundMicroRNAs (miRNAs) have been shown to be involved in the initiation, progression, and prevention of acute myocardial infarction (AMI), but the underlying mechanism remains unclear.ObjectiveThrough the GEO database, bioinformatics methods were used to explore the miRNA-mRNA regulatory relationship pairs associated with AMI and to elucidate the underlying mechanism.MethodsUsing the R software Limma package, differential expression analysis was performed using the AMI-related miRNA chip dataset (GSE31568) and mRNA chip dataset (GSE159657) from the GEO database. The miRDB, miRWalk, miRTarBase, and TargetScan databases were used to predict potential downstream target genes regulated by differentially expressed miRNAs, and a miRNA-mRNA regulatory network was built with Cytoscape; GO function and KEGG pathway enrichment analyses of target genes were done with Funrich software, and the protein interaction network of target genes in the regulatory network was built with the STRING database.Results and conclusionsA total of 187 differentially expressed miRNAs were experimentally screened, of which 91 were upregulated (such as hsa-miR-302b, hsa-miR-1299), and 96 were downregulated (such as hsa-miR-1201, hsa-miR-1283); 507 differentially expressed mRNAs were identified, of which 430 were upregulated (such as MRM1 and SFXN4), and 77 were downregulated (such as KCTD13 and CCDC134). And 16 miRNAs and 44 mRNAs were used for regulatory network construction. GO and KEGG enrichment analyses mainly focused on Integrins in angiogenesis, angiopoietin receptor Tie2-mediated signaling, and signaling events mediated by stem cell factor receptor (c-Kit). As hub genes in the PPI network, FGF2 and MMP2 may be key targets of AMI. The experimentally constructed miRNA-mRNA regulatory network found that hsa-miR-190b targets to inhibit FGF2, while hsa-miR-330-3p targets to regulate MMP2, which may mediate Integrins in angiogenesis, Angiopoietin receptor Tie2-mediated signaling pathway to induce AMI pathogenesis, providing strong data support and a research direction for the prevention and treatment of AMI.

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