Dataset Information

Identification of circRNA-miRNA-mRNA Regulatory Network and Autophagy Interaction Network in Atrial Fibrillation Based on Bioinformatics Analysis.

ABSTRACT:

Background

Circular RNA (circRNA) has been receiving increased attention in the research of atrial fibrillation (AF). Our study aims to find potential circRNAs and identify the circRNA-miRNA-mRNA regulatory network in AF based on bioinformatics analysis.

Methods

GSE129409 was retrieved from the Gene Expression Omnibus (GEO) database, and we used R software to analyze the differentially expressed circRNAs (DECs). Subsequently, we used several bioinformatics methods to obtain the target miRNAs and the target genes. Next, we performed Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the target genes. Then, we used Cytoscape 3.8.2 software to visualize and construct the circRNA-miRNA-mRNA regulatory network, the protein-protein interaction (PPI) network, and the autophagy-related genes network.

Results

We identified a total of 21 DECs, including 6 upregulated DECs and 15 downregulated DECs. After further analysis, we obtained a circRNA-miRNA-mRNA regulatory network consisting of 11 DECs, 9 target miRNAs and 410 target genes, and a PPI network. Finally, the potential novel genes of autophagy in AF were revealed by bioinformatics analysis.

Conclusion

This study could explore the potential role of circRNA, autophagy-related genes and construct the circRNA-miRNA-mRNA regulation network in AF.

SUBMITTER: Chao X

PROVIDER: S-EPMC8612294 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA-miRNA-mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein-protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug-gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA-miRNA-mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA-miRNA-hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks.

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