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?

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.