Construction of Circular RNA–MicroRNA–Messenger RNA Regulatory Network of Recurrent Implantation Failure to Explore Its Potential Pathogenesis

Abstract

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

Comprehensive Analysis of circRNA-Mediated ceRNA Regulatory Networks in relation to Recurrent Implantation Failure

“…Based on the DE-circRNA and DE-miRNA interactions in RIF, there were several circRNAs showing same expression patterns as KDR, POSTN, and RRM2 in RIF were identified, including hsa_circ_001572, hsa_circ_001884, hsa_circ_001375, hsa_circ_001449, hsa_circ_000029, hsa_circ_001168, hsa_circ_000210, hsa_circ_001484, hsa_circ_001698, and hsa_circ_000089. Actually, the circRNAs we found were different from the previous evidence revealing circRNAs (hsa_circ_0058161, hsa_circ_0033392, hsa_circ_0030162, hsa_circ_0004121, hsa_circ_0034642, and hsa_circ_0034762) participate in pathogenesis of RIF [ 26 ]. It suggested that our circRNAs might be novel biomarkers of RIF.…”

Role of endometrial microRNAs in repeated implantation failure (mini-review)

“…MiR-1290 reduces YWHAZ in RIF patients. An increase in miR-375 and miR-1305 has also been reported in RIF (Luo et al, 2021).…”

Integrated bioinformatics analysis to identify key genes and pathways involved in the endometria of patients with recurrent implantation failure undergoing in vitro fertilization and embryo transfer

“…Studies have shown that a variety of factors, including morphological changes, appearance of pinopodes, leukemia inhibitory factors, cyclins, and intrinsic endometrial molecular mechanisms, may affect endometrial receptivity [5][6][7][8][9] . Furthermore, an increasing number of studies have investigated the endometrial gene expression profiles using microarrays or RNA sequencing to identify biomarkers for predicting implantation and diagnosing RIF [10][11][12][13][14][15][16][17][18][19][20] . However, the results of existing studies are inconsistent because of differences in the inclusion criteria for RIF and controls, time of endometrial biopsy, and platforms and methods used for bioinformatic analyses.…”