Gene pathway analysis of the endometrium at the start of the window of implantation in women with unexplained infertility and unexplained recurrent pregnancy loss: is unexplained recurrent pregnancy loss a subset of unexplained infertility?

Unveiling the molecular mechanisms of recurrent miscarriage through endoplasmic reticulum stress related gene expression

Recurrent miscarriage (RM) is a reproductive disorder affecting couples worldwide. The underlying molecular mechanisms remain elusive, even though emerging evidence has implicated endoplasmic reticulum stress (ERS). We investigated RM- and ERS-related genes to develop a diagnostic model that can enhance predictive ability. We utilized the R package GEO query to extract and process Gene Expression Omnibus data, applying batch correction, normalization, and differential gene expression analysis with limma. ERS-related differentially expressed genes (ERSRGs) were identified through Gene Ontology and Kyoto Encyclopedia of genes and genomes analyses, and their diagnostic potential was assessed. Diagnostic models were developed using logistic regression, support vector machines, and least absolute shrinkage and selection operators, complemented by immune infiltration analysis and regulatory network construction. Integrated analysis revealed 1395 differentially expressed genes (DEGs), including 626 upregulated and 769 downregulated genes. Seventeen ERSRGs were identified. KEAP1 and YIPF5 displayed high diagnostic accuracy (area under the curve [AUC] > 0.9). Gene Ontology and Kyoto Encyclopedia of genes and genomes analyses highlighted the role of ESRDEGs in cellular responses to ERS, protein processing, and apoptosis. Diagnostic models demonstrated robust predictive performance (AUC > 0.9). A molecular interaction was found between RM and the ERS response, and the identified ESRDEGs could serve as potential biomarkers for diagnosis.

FXYD1 was identified as a hub gene in recurrent miscarriage and involved in decidualization via regulating Na/K-ATPase activity

PURPOSE

Recurrent miscarriage (RM) is a distressing and complicated adverse pregnancy outcome. It is commonly recognized that insufficient decidualization could result in RM, but the molecular mechanisms of decidual impairment are still not fully understood. Thus, this study aimed to identify novel key genes potentially involved in RM and explore their roles played in endometrial decidualization.

METHODS

Initially, a combinative analysis of decidual and mid-secretory endometrial transcriptomes was performed to discover hub genes involved in the etiology of RM. And the expression levels of hub genes were evaluated in both primary decidual stromal cells (DSCs) and decidual tissues. Subsequently, the immortalized human endometrial cell line, T-HESCs, was used to investigate whether FXYD1 overexpression affects decidualization by regulating Na/K-ATPase activity.

RESULTS

FXYD domain containing ion transport regulator 1 (FXYD1) was identified as a hub gene in the pathogenesis of RM through various bioinformatic methods. Abnormally increased FXYD1 expression was observed in DSCs and decidual tissues from RM patients compared to that of the normal group. Furthermore, in vitro decidualization was obviously inhibited by the overexpression of FXYD1. Additionally, Na/K-ATPase activity was significantly elevated during decidualization, whereas overexpression of FXYD1 reduced Na/K-ATPase activity. Bufalin, a Na/K-ATPase inhibitor, showed an effectively inhibitory effect on decidualization.

CONCLUSIONS

Collectively, FXYD1 was discovered as a hub gene associated with RM, and its expression levels in RM patients were significantly upregulated. Increased FXYD1 expression might lead to decidualization defects by reducing Na/K-ATPase activity, of which presented a novel prospective treatment target for RM.

Identification of Biomarkers and Mechanisms Associated with Apoptosis in Recurrent Pregnancy Loss

In this study, we employed bioinformatics techniques to identify genes associated with apoptosis in recurrent pregnancy loss (RPL). We retrieved the RPL expression profile datasets GSE165004 and GSE73025 from the Gene Expression Omnibus (GEO) database. We also obtained data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway of Apoptosis (hsa04210) to identify apoptosis-related genes. In addition, we performed Friends analysis to explore the interactions between differential apoptosis genes and other genes in the functional pathway. We identified six differentially expressed genes related to apoptosis, including CTSZ, BCL2, PIK3CD, KRAS, GADD45G, and CASP8, with GADD45G as the most gene. Functional fertility analysis revealed that differentially expressed genes primarily regulated protein stability, cell number homeostasis, myeloid cell homeostasis, hematopoietic progenitor cell differentiation, lytic vacuole and lysosome functions, vacuolar and lysosomal membranes, transmembrane transporter binding, protein domain-specific binding, G-protein beta-subunit binding, phospholipid binding, and were involved in pathways such as Rap1 signaling, regulation of actin cytoskeleton, and NOD-like receptor signaling. KRAS exhibited the highest mutation rate in RPL-related cancer CESC. There was also a positive correlation between differentially expressed genes and B cell memory, CD4 memory resting T cells, follicular helper T cells, naïve B cells, and resting dendritic cells. We identified six differentially expressed genes related to apoptosis in RPL, with GADD45G as the most important. NOD-like receptor signaling pathway and regulation of actin cytoskeleton could be therapeutic targets for RPL.

Identification and RT-qPCR Validation of Biomarkers Based on Butyrate Metabolism-Related Genes to Predict Recurrent Miscarriage

Purpose

To date, the cause of recurrent miscarriage (RM) in at least 50% of patients remains unknown. However, no study has explored the correlation between butyrate metabolism-related genes (BMRGs) and RM.

Methods

RM-related datasets (GSE165004, GSE111974, GSE73025, and GSE179996) were obtained from the Gene Expression Omnibus (GEO) database. First, 595 differentially expressed genes (DEGs) were identified between the RM and control samples in GSE165004. Subsequently, 213 differentially expressed BMRGs (DE-BMRGs) were identified by considering the intersection of DEGs with BMRGs. The protein-protein interaction (PPI)network of DE-BMRGs contained 156 nodes and 250 edges, and a key module was obtained. In total, four biomarkers (ACTR2, ANXA2, PFN1, and OAS1) were acquired through least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF). Immune analysis revealed two immune cells and three immune-related gene sets that were significantly different between the RM and control groups, namely, T helper cells, regulatory T cells (Treg), MHC class I, parainflammation, and type I IFN response. In addition, a TF-mRNA network based on the top 100 nodes ranked in the order of connectivity was created, including 100 nodes and 253 edges, such as MTERF2-ACTR2, NKX23-PFN1, STAT1-OAS1, and SP100-ANXA2.

Results

Finally, 3 drugs (withaferin A, N-ethylmaleimide, and etoposide) were predicted to interact with 2 biomarkers (ANXA2 and ACTR2). Eventually, ANXA2 and OAS1 were significantly downregulated, and PFN1 was markedly overexpressed in the RM group, as determined by reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Conclusion

Our findings authenticated four butyrate metabolism-related biomarkers for the diagnosis of RM, providing a scientific reference for further studies on RM treatment.

Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis

Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.

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.

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA

Background

Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications.

Methods

The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap.

Results

A total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs.

Conclusion

This study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

Assisted reproductive technology outcomes and gene expression in unexplained infertility patients

Background: Unexplained infertility (UI) can be a frustrating and challenging diagnosis for doctors and couples as it can be difficult to understand why they are unable to conceive despite increasing diagnostic tools. Assisted reproductive technology (ART) procedures have been successfully applied to many couples aiming to overcome UI. However, they can be not only expensive but also require multiple cycles to achieve a successful pregnancy. The endometrium and the follicular fluid have been investigated as target tissues not only to determine the cause of UI but also to increase conception rates. Results: In this study, we analyzed the outcomes of ART in 223 UI couples and gene expression associated with DNA modification, cell death, immune response and senescence (TET1, TET2, BCL2, BAK1, HMGA2, IL-6, IL-8) in infertile women's endometrium and follicular fluid. We found significant differences in women who successfully got pregnant compared to women unable to conceive depending on age, duration of infertility, number of retrieved oocytes, zygotes, transferred embryos. Further, the expression of genes BAK1 (pro-apoptotic), TET2 (associated with epigenetic DNA modification) and IL-6 (associated with immune responses) were significantly higher in the endometrium of women who successfully got pregnant. Conclusion: Younger parental age couples showed higher ART success rates, shorter duration of infertility, higher number of retrieved oocytes, zygotes and transferred embryos. The gene expression analysis revealed significant changes in the endometrium depending on genes associated with cell death and immune response which were upregulated in females with diagnosed unexplained infertility.