Construction of lncRNA-related competing endogenous RNA network and identification of hub genes in recurrent implantation failure

FANCD2 as a ferroptosis-related target for recurrent implantation failure by integrated bioinformatics and Mendelian randomization analysis

Despite advancements in assisted reproductive technology, recurrent implantation failure (RIF) remains a challenge. Endometrial factors, including ferroptosis and immunity, may contribute to this issue. This study integrated bioinformatics analysis and Mendelian randomization (MR) to investigate the expression and significance of DEFRGs in RIF. We intersected 484 ferroptosis-associated genes with 515 differentially expressed genes (DEGs) to identify key DEFRGs. Subsequent analyses included enrichment analysis, molecular subtype identification, machine learning model development for biomarker discovery, immune cell infiltration assessment, single-cell RNA sequencing, and MR to explore the causal relationships of selected genes with RIF. In this study, we identified 11 differentially expressed ferroptosis-related genes (DEFRGs) between RIF and healthy individuals. Cluster analysis revealed two distinct molecular subtypes with different immune profiles and DEFRG expressions. Machine learning models highlighted MUC1, GJA1 and FANCD2 as potential diagnostic biomarkers, with high accuracy in RIF prediction. Single-cell analysis further revealed the cellular localization and interactions of DEFRGs. MR suggested a protective effect of FANCD2 against RIF. Validation in RIF patients confirmed the differential expression of key DEFRGs, consistent with bioinformatics findings. This comprehensive study emphasize the significant role of DEFRGs in the pathogenesis of RIF, suggesting that modulating these genes could offer new avenues for treatment. The FANCD2 is a potential gene contributing to RIF pathogenesis through a non-classical ferroptosis-dependent pathway, providing a foundation for personalized therapeutic strategies in RIF management.

miR-665-Mediated Regulation of AHCYL2 and BVES Genes in Recurrent Implantation Failure

The primary goal of this investigation was to identify mRNA targets affected by dysregulated miRNAs in RIF. This was accomplished by comprehensively analyzing mRNA and miRNA expression profiles in two groups: female subjects with normal reproductive function (control, n = 5) and female subjects experiencing recurrent implantation failure (RIF, n = 5). We conducted transcriptome sequencing and small RNA sequencing on endometrial tissue samples from these cohorts. Subsequently, we validated a selection of intriguing findings using real-time PCR with samples from the same cohort. In total, our analysis revealed that 929 mRNAs exhibited differential expression patterns between the control and RIF patient groups. Notably, our investigation confirmed the significant involvement of dysregulated genes in the context of RIF. Furthermore, we uncovered promising correlation patterns within these mRNA/miRNA pairs. Functional categorization of these miRNA/mRNA pairs highlighted that the differentially expressed genes were predominantly associated with processes such as angiogenesis and cell adhesion. We identified new target genes that are regulated by miR-665, including Blood Vessel Epicardial Substance (BVES) and Adenosylhomocysteinase like 2 (AHCYL2). Our findings suggest that abnormal regulation of genes involved in angiogenesis and cell adhesion, including BVES and AHCYL2, contributes to the endometrial dysfunction observed in women with recurrent implantation failure (RIF) compared to healthy women.

Endometrial transcriptome profiling of patients with recurrent implantation failure during hormone replacement therapy cycles

Background

The molecular mechanisms underlying window of implantation (WOI) displacement in patients with recurrent implantation failure (RIF) remain unclear. This study aims to explore the transcriptomic signatures of endometrium with normal and displaced WOIs and to identify the causes of endometrial receptivity (ER) abnormalities and WOI displacement in RIF patients.

Methods

In this study, 40 RIF patients were recruited and underwent personalized embryo transfer (pET) guided by the predicted results of endometrial receptivity diagnosis (ERD) model. Transcriptome analysis of endometrium from patients with clinical pregnancies after pET was performed to identify differentially expressed genes (DEGs) associated with WOI displacement. Gene expression data from HRT and natural cycle endometrium were compared to identify specific gene expression patterns of ER-related genes during WOI.

Results

The ERD results indicated that 67.5% of RIF patients (27/40) were non-receptive in the conventional WOI (P+5) of the HRT cycle. The clinical pregnancy rate in RIF patients improved to 65% (26/40) after ERD-guided pET, indicating the effectiveness of transcriptome-based WOI prediction. Among the 26 patients with clinical pregnancy, the gene expression profiles of P+5 endometrium from advanced (n=6), normal (n=10) and delayed (n=10) WOI groups were significantly different from each other. Furthermore, 10 DEGs identified among P+5 endometrium of 3 groups were involved in immunomodulation, transmembrane transport and tissue regeneration, which could accurately classify the endometrium with different WOIs. Additionally, a large number of ER-related genes showed significant correlation and similar gene expression patterns in P+3, P+5, and P+7 endometrium from HRT cycles and LH+5, LH+7, and LH+9 endometrium from natural cycles.

Conclusion

Our study shows that ER-related genes share similar gene expression patterns during WOI in both natural and HRT cycles, and their aberrant expression is associated with WOI displacements. The improvement of pregnancy outcomes in RIF patients by adjusting ET timing according to ERD results demonstrates the importance of transcriptome-based endometrial receptivity assessment and the clinical efficiency of ERD model.

Non-coding RNAs in Recurrent implantation failure

Recurrent implantation failure (RIF), defined as the inability to achieve conception following multiple consecutive in-vitro fertilization (IVF) attempts, represents a complex and multifaceted challenge in reproductive medicine. The emerging role of non-coding RNAs in RIF etiopathogenesis has only gained prominence over the last decade, illustrating a new dimension to our understanding of the intricate network underlying RIF. Successful embryo implantation demands a harmonious synchronization between an adequately decidualized endometrium, a competent blastocyst, and effective maternal-embryonic interactions. Emerging evidence has clarified the involvement of a sophisticated network of non-coding RNAs, including microRNAs, circular RNAs, and long non-coding RNAs, in orchestrating these pivotal processes. Disconcerted expression of these molecules can disrupt the delicate equilibrium required for implantation, amplifying the risk of RIF. This comprehensive review presents an in-depth investigation of the complex role played by non-coding RNAs in the pathogenesis of RIF. Furthermore, it underscores the vast potential of non-coding RNAs as diagnostic biomarkers and therapeutic targets, with the ultimate goal of enhancing implantation success rates in IVF cycles. As ongoing research continues to unravel the intercalated web of molecular interactions, exploiting the power of non-coding RNAs may offer promising avenues for mitigating the challenges posed by RIF and improving the outcomes of assisted reproduction.

Long noncoding RNAs in human reproductive processes and diseases

Infertility has become a global disease burden. Although assisted reproductive technologies are widely used, the assisted reproduction birth rate is no more than 30% worldwide. Therefore, understanding the mechanisms of reproduction can provide new strategies to improve live birth rates and clinical outcomes of enhanced implantation. Long noncoding RNAs (lncRNAs) have been reported to exert regulatory roles in various biological processes and diseases in many species. In this review, we especially focus on the role of lncRNAs in human reproduction. We summarize the function and mechanisms of lncRNAs in processes vital to reproduction, such as spermatogenesis and maturation, sperm motility and morphology, follicle development and maturation, embryo development and implantation. Then, we highlight the importance and diverse potential of lncRNAs as good diagnostic molecular biomarkers and therapeutic targets for infertility treatment.

Emerging role of lncRNAs in the etiology of recurrent implantation failure

Recurrent implantation failure (RIF) is a complex clinical entity with several molecular pathways contributing to its pathogenesis. Long non-coding RNAs (lncRNAs) have recently been found to affect the normal implantation, thus aberrant expression of these transcripts is involved in RIF. Altered expression of HOXA11-AS, NONHSAT193031.1, NONHSAT053761.2, NONHSAT083203.2, LUCAT1, PART1, TUNAR, LINC02190, lncSAMD11-1:1 and H19 has been reported in this condition. Moreover, polymorphisms within some lncRNAs have been shown to be associated with miscarriage/RIF. The current review article summarizes the recent data about the role of lncRNAs in RIF. This information would pave the way for identification of the molecular events in this context.

Friend leukemia integration 1 overexpression decreases endometrial receptivity and induces embryo implantation failure by promoting PART1 transcription in the endometrial epithelial cells

Background

In vitro fertilization-embryo transfer (IVF-ET) is a crucial assisted reproductive technology for treating infertility. However, recurrent implantation failure (RIF), a significant challenge in IVF-ET success, remains unresolved. This study aimed to explore the role and mechanism of FLI1 in endometrial receptivity and RIF.

Methods

Differential endometrial cell proportions between patients with RIF and control subjects were assessed using single-cell RNA sequencing (scRNA-seq) analysis. The chromatin accessibility of FLI1 in the luteal endometrial tissue of patients with RIF and control subjects was examined using the single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq). FLI1 mRNA and protein levels were gauged by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Cell viability and migration were examined via cell counting kit (CCK)-8 and scratch healing assays. Epithelial-mesenchymal transition markers were analyzed using western blotting. Mechanisms underlying FLI1's regulation of PART1 transcription and expression in endometrial epithelial cells were explored using chromatin immunoprecipitation and dual-luciferase reporter assays. Adeno-associated virus (AAV) carrying epithelial cell-specific FLI1/PART1 overexpression sequences was uterinely injected in mice to assess FLI1/PART1 effects.

Results

scRNA-seq revealed diminished endometrial epithelial cell proportions in RIF patients. Meanwhile, scATAC-seq indicated enhanced chromatin accessibility of FLI1 in these cells. FLI1 exhibited specific expression in RIF patients' endometrial epithelial cells. Specific FLI1 overexpression inhibited embryo implantation, while knockdown enhanced it. Pregnant mice injected with AAV encoding FLI1 overexpression had significantly lower implantation than AAV-negative controls. FLI1 binding to PART1 promoter heightened PART1 transcription and expression in endometrial epithelial cells. Rescue experiments illustrated FLI1's role in embryo implantation by boosting PART1 expression. PART1 was notably elevated in RIF patients' luteal endometrial tissue and non-receptive endometrial epithelial cells (HEC-1-A). Specific PART1 overexpression dampened embryo implantation, whereas knockdown promoted it. Pregnant mice injected with AAV encoding PART1 had lower implantation than negative controls. PART1 knockdown mitigated FLI1's inhibitory impact on HEC-1-A cell viability and migration.

Conclusions

FLI1 overexpression in the endometrial epithelial cells of patients with RIF inhibited embryo implantation by binding to the PART1 promoter region to promote PART1 expression. These findings can aid in the development of novel therapeutic targets for RIF.

Recurrent Implantation Failure: Bioinformatic Discovery of Biomarkers and Identification of Metabolic Subtypes

Recurrent implantation failure (RIF) is a challenging scenario from different standpoints. This study aimed to investigate its correlation with the endometrial metabolic characteristics. Transcriptomics data of 70 RIF and 99 normal endometrium tissues were retrieved from the Gene Expression Omnibus database. Common differentially expressed metabolism-related genes were extracted and various enrichment analyses were applied. Then, RIF was classified using a consensus clustering approach. Three machine learning methods were employed for screening key genes, and they were validated through the RT-qPCR experiment in the endometrium of 10 RIF and 10 healthy individuals. Receiver operator characteristic (ROC) curves were generated and validated by 20 RIF and 20 healthy individuals from Peking University People's Hospital. We uncovered 109 RIF-related metabolic genes and proposed a novel two-subtype RIF classification according to their metabolic features. Eight characteristic genes (SRD5A1, POLR3E, PPA2, PAPSS1, PRUNE, CA12, PDE6D, and RBKS) were identified, and the area under curve (AUC) was 0.902 and the external validated AUC was 0.867. Higher immune cell infiltration levels were found in RIF patients and a metabolism-related regulatory network was constructed. Our work has explored the metabolic and immune characteristics of RIF, which paves a new road to future investigation of the related pathogenic mechanisms.

Bioinformatics Analysis Identifies Key Genes in Recurrent Implantation Failure Based on Immune Infiltration

Recurrent implantation failure (RIF) is a thorny problem often encountered in the field of assisted reproduction. Existing evidences suggest that immune dysregulation may be involved in the pathogenesis of RIF. The purpose of this study is to explore immune-related genes contributing to RIF through data mining. The endometrial expression profiles of 24 RIF and 24 controls were obtained from the GEO database. The immune infiltration in bulk tissue was estimated by single sample gene set enrichment analysis (ssGSEA) method based on marker gene sets for immune cells generated from endometrial single-cell RNA sequencing data. The results showed that the infiltration levels of B cells and regulatory T cells (Tregs) were significantly reduced in the RIF group. Four hub genes (GJA1, PRKAG2, CPT1A, and ICA1) were identified by integrated analysis of weighted gene co-expression network analysis (WGCNA), random forest and LASSO regression. Moreover, these hub genes were significantly correlated with certain immune-related factors, especially CXCL12, CEACAM1, and XCR1. Single-gene GSEA indicated that the pathways associated with hub genes included the regulation of cell cycle, the process of epithelial-mesenchymal transition and transplant rejection, etc. A predictive model for RIF was constructed based on hub genes and performed well in the training dataset and the other two external datasets. Thus, this study identified immune-related key genes in RIF and provided new biomarkers for early diagnosis.

Immune alterations in recurrent implantation failure

A failure to achieve pregnancy after three or more embryo transfer cycles with high-quality blastocysts is referred to as recurrent implantation failure (RIF). RIF can be due to altered uterine factors or male factors or embryo factors. Disrupted endometrial receptivity, altered expression of genes in several pathways, immunologic disturbances in the peripheral blood and/or the endometrium, and epigenetic alterations are associated with RIF. Amongst the immunologic disturbances, altered Th1/Th2 ratio, altered NK cell and macrophage numbers are observed in women with RIF. However, not all women with RIF have the same kind of immune dysfunction suggesting that RIF is a heterogeneous condition associated with varied immune responses and one size may not fit all. Thus, personalized therapies based on the immune status of the patient are being tested in women with RIF. In general, women with a high Th1/Th2 ratio are offered Tacrolimus, while intravenous IgG is recommended in women with high NK cell numbers/HLA mismatch. Women with hyperactivated immune status in the uterus are offered progesterone support, prednisolone, vitamin E, and intralipid treatment to suppress inflammation and oxidative stress, while endometrial scratching and intrauterine hCG administration are offered to women with hypo-active immune status. There is a need for standardized tests for evaluation of immune status in patients and sufficiently powered randomized controlled trials for personalized therapies to determine which of these will be beneficial in women with RIF. Till then, the ART community should limit the use of such add-on interventions in women with RIF.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.