Dataset Information

Precise hourly personalized embryo transfer significantly improves clinical outcomes in patients with repeated implantation failure

ABSTRACT: This was a retrospective study enrolling patients with a history of RIF who received at least one autologous high-quality blastocyst during the subsequent FET cycle. Patients were divided into two groups: Group rsERT, comprising patients who underwent rsERT-guided pET (n=115), and Group FET, comprising patients who underwent standard FET without rsERT (n=272). Of the patients who underwent rsERT, 39.1% (45/115) were receptive, and 60.9% (70/115) were non-receptive. Group rsERT patients showed a significantly higher probability of achieving both a positive human chorionic gonadotropin rate (63.5% vs. 51.5%, P=0.03) and clinical pregnancy rate (54.8% vs. 38.6%, P=0.003). In the subgroup analysis, Group rsERT patients with non-receptive results showed a significantly higher clinical pregnancy rate than patients in Group FET (58.6% vs. 38.6%, P=0.003). Furthermore, Group rsERT patients with receptive results guided by rsERT with a precise window-of-implantation time showed a higher clinical pregnancy rate (48.9% vs. 38.6%, P=0.192) compared with patients who underwent standard-time FET, although the difference was not significant. Hourly precise rsERT can significantly improve the probability of achieving clinical pregnancy in patients with RIF, especially in those with non-receptive rsERT results.

ORGANISM(S): Homo sapiens

PROVIDER: GSE245473 | GEO | 2025/10/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: This study investigates molecular differences between receptive and non-receptive endometrium by analyzing RNA cargo of uterine fluid-derived extracellular vesicles (UF-EVs). A total of 82 women who underwent blastocyst transfer with confirmed euploid embryos via Pre-implantation Genetic Testing (PGT-A) were included and followed to assess clinical pregnancy outcomes. The aim is to identify the transcriptomic profile of the endometrium during the window of implantation (WOI) by comparing gene expression between patients who achieved pregnancy and those who did not, and to use the WOI gene expression profile to predict pregnancy outcomes through a Bayesian logistic model. Methods: UF samples were collected on day 7 after the urinary LH surge (LH+7) in the cycle preceding the scheduled blastocyst transfer. In the subsequent natural cycle (LH+7), an ultrasound-guided transfer of the euploid blastocyst was performed, and clinical outcomes were recorded. A Differential Gene Expression (DGE) analysis (pregnant vs. not pregnant) was conducted, followed by a gene co-expression network analysis (WGCNA), which identified modules of co-expressed genes. These gene modules were integrated with clinical data to develop a Bayesian logistic regression model. Results: UF-EVs collected from 82 women were analyzed. Among the patients, 37 achieved pregnancy, while 45 did not. Differential gene expression analysis identified 966 significant genes (nominal p < 0.05), with a predominant upregulation in the pregnant group. Applying SEQC thresholds (nominal p < 0.01, |log₂FC| > 1) refined the list to 262 differentially expressed genes, the majority of which were overexpressed in patients who conceived. Four genes,RPL10P9, LINC00621, MTND6P4, and LINC00205, remained significant after multiple testing correction. Gene Set Enrichment Analysis (GSEA) revealed enriched biological processes such as adaptive immune response, lymphocyte-mediated immunity, and ion homeostasis. Weighted Gene Co-expression Network Analysis (WGCNA) grouped differentially expressed genes into four modules, with the grey and brown modules showing the strongest positive correlation with pregnancy. Functional enrichment of these modules highlighted immune regulation, transcriptional activity, and extracellular matrix remodeling. A Bayesian logistic regression model integrating gene expression modules with clinical variables accurately predicted pregnancy outcomes with 82.9% accuracy, an F1-score of 0.80, and an AUC of 0.88. Positive associations were observed for the grey module and co-expression patterns in the brown and turquoise modules, while increased EVs size and a history of miscarriages were negatively associated with pregnancy. Conclusions: This study demonstrates that UF-EV transcriptomic profiling can non-invasively capture endometrial receptivity signatures during the WOI. The integration of molecular and clinical data enables accurate pregnancy prediction, offering a promising approach to personalize embryo transfer timing and improve outcomes in ART.

Dataset Information

Precise hourly personalized embryo transfer significantly improves clinical outcomes in patients with repeated implantation failure

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets