Identification of Key Endometrial MicroRNAs and Their Target Genes Associated With Pathogenesis of Recurrent Implantation Failure by Integrated Bioinformatics Analysis

• Published in: Frontiers in genetics 2022-06, Vol.13, p.919301-919301
• Main Authors: Shang, Jin, Cheng, Yan-Fei, Li, Min, Wang, Hui, Zhang, Jin-Ning, Guo, Xin-Meng, Cao, Dan-dan, Yao, Yuan-Qing
• Format: Article
• Language: English
• Subjects: bioinformatics; differentially expressed genes; differentially expressed miRNAs; endometrial transcriptomics; Genetics; recurrent implantation failure
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2022.919301

Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation. Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA–target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA–target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR). Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA–target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p- PDPN and hsa-miR-4306- PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR ( p < 0.05). Conclusion: We constructed miRNA–target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p- PDPN and hsa-miR-4306- PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.