Construction of a circRNA-miRNA-mRNA Regulatory Network for Coronary Artery Disease by Bioinformatics Analysis

Zebo Zhang; Haiyan Qian; Li Wang; Zhenbo Tao; Keai Cheng; Kaiyue Wang; Yanqing Xie; Lina Zhang

doi:10.1155/2022/4017082

Construction of a circRNA-miRNA-mRNA Regulatory Network for Coronary Artery Disease by Bioinformatics Analysis

Cardiol Res Pract. 2022 Feb 16:2022:4017082. doi: 10.1155/2022/4017082. eCollection 2022.

Authors

Zebo Zhang¹, Haiyan Qian¹, Li Wang², Zhenbo Tao¹, Keai Cheng², Kaiyue Wang², Yanqing Xie², Lina Zhang¹

Affiliations

¹ Department of Preventative Medicine, Zhejiang Key Laboratory of Pathophysiology, Medical School of Ningbo University, Ningbo, Zhejiang Province 315211, China.
² Insitute of Geriatrics, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang Province 315100, China.

Abstract

Background: Circular RNAs (circRNAs) were known to be related to the pathogenesis of many diseases through competing endogenous RNA (ceRNA) regulatory mechanisms. However, the function of circRNA in coronary artery disease (CAD) remains unclear. In this study, we aim to construct a circRNA-related competing endogenous RNA (ceRNA) network in CAD.

Methods: The gene expression profiles of CAD were obtained from Gene Expression Omnibus datasets. Bioinformatics analysis was performed to construct a ceRNA regulatory network, from which the hub genes involved were identified through protein-protein interaction (PPI) networks leading to the identification of the circRNA-miRNA-hub gene subnetwork. In addition, function enrichment analysis was performed to detect the potential biological mechanism in which circRNA might be involved.

Results: A total of 115 DEcircRNAs (differentially expressed circRNAs), 17 DEmiRNAs (differentially expressed microRNAs), and 790 DEmRNAs (differentially expressed mRNAs) were identified between CAD and control groups from microarray datasets. Functional enrichment analysis showed that DEmRNAs were significantly involved in inflammation-related pathways and ubiquitin-protein ligase binding. After identifying 20 DEcircRNA-DEmiRNA pairs and 561 DEmiRNA-DEmRNA pairs, we obtained a circRNA-miRNA-mRNA regulatory network. PPI network analysis showed that eight hub genes were closely related to CAD, leading to the identification of a circRNA-miRNA-hub gene subnetwork consisting of nine circRNAs (hsa_circ_0020275, hsa_circ_0020387, hsa_circ_0020417, hsa_circ_0045512, hsa_circ_0047336, hsa_circ_0069094, hsa_circ_0071326, hsa_circ_0071330, and hsa_circ_0085340), four miRNAs (hsa-miR-136-5p, hsa-miR-376c-3p, hsa-miR-411-5p, and hsa-miR-654-5p), and eight mRNAs (MKRN1, UBE2H, UBE2W, UBE2D1, UBE2F, BE2J1, ZNRF1, and SIAH2). In addition, we discovered these hub genes were enriched in the ubiquitin-mediated proteolysis pathway, suggesting circRNAs may be involved in the pathogenesis of CAD through this pathway.

Conclusions: This study may deepen our understanding of the potential role of circRNA-miRNA-mRNA regulation network in CAD and suggest novel diagnostic biomarkers and therapeutic targets for CAD.