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

Meta-analysis of endometrial transcriptome data reveals novel molecular targets for recurrent implantation failure

PURPOSE

Gene expression analysis of the endometrium has been shown to be a useful approach for identifying the molecular signatures and pathways involved in recurrent implantation failure (RIF). Nevertheless, individual studies have limitations in terms of study design, methodology and analysis to detect minor changes in expression levels or identify novel gene signatures associated with RIF.

METHOD

To overcome this, we conducted an in silico meta-analysis of nine studies, the systematic collection and integration of gene expression data, utilizing rigorous selection criteria and statistical techniques to ensure the robustness of our findings.

RESULTS

Our meta-analysis successfully unveiled a meta-signature of 49 genes closely associated with RIF. Of these genes, 38 were upregulated and 11 downregulated in RIF patients' endometrium and believed to participate in key processes like cell differentiation, communication, and adhesion. GADD45A, IGF2, and LIF, known for their roles in implantation, were identified, along with lesser-studied genes like OPRK1, PSIP1, SMCHD1, and SOD2 related to female infertility. Many of these genes are involved in MAPK and PI3K-Akt pathways, indicating their role in inflammation. We also investigated to look for key miRNAs regulating these 49 dysregulated mRNAs as potential diagnostic biomarkers. Along with this, we went to associate protein-protein interactions of 49 genes, and we could recognize one cluster consisting of 11 genes (consisted of 22 nodes and 11 edges) with the highest score (p = 0.001). Finally, we validated some of the genes by qRT-PCR in our samples.

CONCLUSION

In summary, the meta-signature genes hold promise for improving RIF patient identification and facilitating the development of personalized treatment strategies, illuminating the multifaceted nature of this complex condition.

Construction of hub transcription factor-microRNAs-messenger RNA regulatory network in recurrent implantation failure

PURPOSE

Recurrent implantation failure (RIF) affects up to 10% of in vitro fertilization (IVF) patients worldwide. However, the pathogenesis of RIF remains unclear. This study was aimed at identifying hub transcription factors (TFs) of RIF in bioinformatics approaches.

METHODS

The GSE111974 (mRNA), GSE71332 (miRNA), and GSE103465 (mRNA) datasets were downloaded from the Gene Expression Omnibus database from human endometrial tissue using R version 4.2.1 and used to identify differentially expressed TFs (DETFs), differentially expressed miRNAs, and differentially expressed genes for RIF, respectively. DETFs were subjected to functional enrichment analysis and the protein-protein interaction network analysis using the Search Tool for the Retrieval of Interacting Genes (version 11.5) database. Hub TFs were identified using the cytoHubb plug-in, after which a hub TF-miRNA-mRNA network was constructed using Cytoscape v3.8.2.

RESULTS

Fifty-seven DETFs were identified, in which Gene Ontology analysis revealed to be mainly involved in the regulation of transcription. Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that DETFs were enriched in transcriptional misregulation in cancer, aldosterone synthesis and secretion, AMPK signaling pathway, and cGMP-PKG signaling pathway. EOMES, NKX2-1, and POU5F1 were identified as hub TFs, and a hub TF-miRNA-mRNA regulatory network was constructed using these three hub TFs, four miRNAs, and four genes.

CONCLUSION

Collectively, we identified three promising molecular biomarkers for the diagnosis of RIF, which may further be potential therapeutic targets. This study provides novel insights into the molecular mechanisms underlying RIF. However, further experiments are required to verify these results.

Research progress in the role of non-coding RNAs and embryo implantation

Non-coding RNA (ncRNA) refers to RNA that lack the ability to encode protein. Based on their distinct biological characteristics, ncRNA are mainly classified into microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). NcRNA plays a crucial regulatory role in various biological processes. Pregnancy is a highly intricate physiological process that requires successful completion of multiple steps. Embryo implantation, as a key event of pregnancy, which is regulated by numerous factors, including embryo development, endometrial changes, and the maternal-embryo crosstalk. A diverse array of regulatory mechanisms ensures the accomplishment of embryo localization, adhesion, invasion, and ultimately successful implantation. MiRNA, lncRNA, and circRNA are extensively studied ncRNA molecules at present, which play an important role in the physiological and pathological processes associated with embryo implantation through targeting and regulating the expression of multiple cytokine and genes. With advancements in molecular biology technology, it is anticipated that ncRNA will contribute to the prediction and enhancement of clinical pregnancy outcomes from a molecular perspective.

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

Recurrent implantation failure (RIF) is attributed to endometrial receptivity dysfunction with many unanswered questions. Up to now, there is no explanation for RIF, and therapeutic strategies are usually limited to supportive care. In this study, we differentially analyzed the raw data deposited in three eligible microarray datasets, GSE111974, GSE121219, and GSE147442 to screen DE-mRNAs, DE-miRNAs, and DE-circRNAs, respectively. The value of log2-fold change |log2FC| ≥ 1 and the adjusted p value < 0.05 were considered differentially expressed between RIF and fertile control. We found 350 DE-mRNAs, 43 DE-miRNAs, and 1968 DE-circRNAs between RIF and fertile control. The PPI network identified 6 hub genes with degree ≥10, KDR, AGT, POSTN, TOP2A, RRM2, and PTGS2, in RIF. KDR, AGT, POSTN, TOP2A, and RRM2 were downregulated in endometrial tissue samples of RIF compared with those of fertile control, while PTGS2 was upregulated in endometrial tissue samples of RIF compared with those of fertile control. According to the ceRNA hypothesis, 15 groups of ceRNA network based on 10 circRNAs, 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 were constructed in RIF. In conclusion, the present study examined the possible role of circRNAs and their related ceRNA network involved in the pathogenesis of RIF.

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

MicroRNAs (miRNAs) play various roles in the implantation and pregnancy process. Abnormal regulation of miRNAs leads to reproductive disorders such as repeated implantation failure (RIF). During the window of implantation, different miRNAs are released from the endometrium, which can potentially reflect the status of the endometrium for in vitro fertilization (IVF). The focus of this review is to determine whether endometrial miRNAs may be utilized as noninvasive biomarkers to predict the ability of endometrium to implant and provide live birth during IVF cycles. The levels of certain miRNAs in the endometrium have been linked to implantation potential and pregnancy outcomes in previous studies. Endometrial miRNAs could be employed as non-invasive biomarkers in the assisted reproductive technology (ART) cycle to determine the optimal time for implantation. Few human studies have evaluated the association between ART outcomes and endometrial miRNAs in RIF patients. This review may pave the way for more miRNA transcriptomic studies on human endometrium and introduce a specific miRNA profile as a multivariable prediction model for choosing the optimal time in the IVF cycle.

Candidate genes for infertility: an in-silico study based on cytogenetic analysis

Background

The cause of infertility remains unclear in a significant proportion of reproductive-age couples who fail to conceive naturally. Chromosomal aberrations have been identified as one of the main genetic causes of male and female infertility. Structural chromosomal aberrations may disrupt the functioning of various genes, some of which may be important for fertility. The present study aims to identify candidate genes and putative functional interaction networks involved in male and female infertility using cytogenetic data from cultured peripheral blood lymphocytes of infertile patients.

Methods

Karyotypic analyses was done in 201 infertile patients (100 males and 101 females) and 201 age and gender matched healthy controls (100 males and 101 females) after 72 h peripheral lymphocyte culturing and GTG banding, followed by bioinformatic analysis using Cytoscape v3.8.2 and Metascape.

Results

Several chromosomal regions with a significantly higher frequency of structural aberrations were identified in the infertile males (5q2, 10q2, and 17q2) and females (6q2, 16q2, and Xq2). Segregation of the patients based on type of infertility (primary v/s secondary infertility) led to the identification of chromosomal regions with a significantly higher frequency of structural aberrations exclusively within the infertile males (5q2, 17q2) and females (16q2) with primary infertility. Cytoscape identified two networks specific to these regions: a male specific network with 99 genes and a female specific network with 109 genes. The top enriched GO terms within the male and female infertility networks were “skeletal system morphogenesis” and “mRNA transport” respectively. PSME3, PSMD3, and CDC27 were the top 3 hub genes identified within the male infertility network. Similarly, UPF3B, IRF8, and PSMB1 were the top 3 hub genes identified with the female infertility network. Among the hub genes identified in the male- and female-specific networks, PSMB1, PSMD3, and PSME3 are functional components of the proteasome complex. These hub genes have a limited number of reports related to their respective roles in maintenance of fertility in mice model and humans and require validation in further studies.

Conclusion

The candidate genes predicted in the present study can serve as targets for future research on infertility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01320-x.

Integrated Analysis of Multiple Microarrays Based on Raw Data Identified Novel Gene Signatures in Recurrent Implantation Failure

BACKGROUND

Recurrent implantation failure (RIF) is an intricate complication following IVF-ET, which refers to the situation that good-quality embryos repeatedly fail to implant following two or more IVF cycles. Intrinsic molecular mechanisms underlying RIF have not yet been fully elucidated. With enormous improvement in high-throughput technologies, researchers screened biomarkers for RIF using microarray. However, the findings of published studies are inconsistent. An integrated study on the endometrial molecular determinants of implantation will help to improve pregnancy outcomes.

OBJECTIVE

To identify robust differentially expressed genes (DEGs) and hub genes in endometrium associated with RIF, and to investigate the diagnostic role of hub genes in RIF.

METHODS

Raw data from five GEO microarrays regarding RIF were analyzed. Integrated genetic expression analyses were performed using the Robust Rank Aggregation method to identify robust DEGs. Enrichment analysis and protein-protein interaction (PPI) analysis were further performed with the robust DEGs. Cytohubba was used to screen hub genes based on the PPI network. GSE111974 was used to validate the expression and diagnostic role of hub genes in RIF.

RESULTS

1532 Robust DEGs were identified by integrating four GEO datasets. Enrichment analysis showed that the robust DEGs were mainly enriched in processes associated with extracellular matrix remodeling, adhesion, coagulation, and immunity. A total of 18 hub genes (HMGCS1, SQLE, ESR1, LAMC1, HOXB4, PIP5K1B, GNG11, GPX3, PAX2, TF, ALDH6A1, IDH1, SALL1, EYA1, TAGLN, TPD52L1, ST6GALNAC1, NNMT) were identified. 10 of the 18 hub genes were significantly differentially expressed in RIF patients as validated by GSE111974. The 10 hub genes (SQLE, LAMC1, HOXB4, PIP5K1B, PAX2, ALDH6A1, SALL1, EYA1, TAGLN, ST6GALNAC1) were effective in predicting RIF with an accuracy rate of 85%, specificity rate of 100%, and sensitivity rate of 88.9%.

CONCLUSIONS

Our integrated analysis identified novel robust DEGs and hub genes in RIF. The hub genes were effective in predicting RIF and will contribute to the understanding of comprehensive molecular mechanisms in RIF pathogenesis.