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From gene expression to pregnancy prediction: towards precision ART through systems biology and Bayesian modeling

GSE297368 Homo sapiens (human) RNA-seq (gene expression) 43 samples Submitted 2025/09/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 43 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
43 samples: 43 × RNA-seq of EVs from uterine fluid , ART patient, sampleGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Endometrial receptivity profiled through transcriptomic analysis of uterine fluid extracellular vesicles using systems biology and bayesian modeling for pregnancy predictionScientific reports · 2025
Summary (written by the submitters)
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.
Read the full summary ↓
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.
Published in
Endometrial receptivity profiled through transcriptomic analysis of uterine fluid extracellular vesicles using systems biology and bayesian modeling for pregnancy prediction
Marzanati D, Ranucci F, Pagliardini L et al. · Scientific reports 2025 · PMID 41083775 · doi:10.1038/s41598-025-19633-z
This dataset
Open GSE297368 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE297368_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1263594 and SRA study SRP586036. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41083775) ↗
Study design
43 × RNA-seq of EVs from uterine fluid , ART patient, sample

Supports a between-group comparison across 43 samples.

How we read this: 1 replicated groups read from 43 sample titles; they account for 43 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 3 more — browse all 43 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.