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Endometriosis Pain: Genes linked to Inflammation Distinguish Symptomatic from Asymptomatic Disease

GSE339400 Homo sapiens (human) RNA-seq (gene expression) 17 samples Submitted 2026/07/28 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 17 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
17 samples: 17 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
Endometriosis is a multifaceted disease, causing debilitating pelvic pain in some patients, while being completely asymptomatic in others. The pathophysiology of endometriosis pain is not well understood and poorly correlated to stage. We hypothesized that this clinical heterogeneity may be explained by examining differences in gene expression.
Read the full summary ↓
We performed RNA sequencing of 27 formalin-fixed, paraffin-embedded peritoneal biopsies from 9 symptomatic (Sx) and 10 asymptomatic (ASx) subjects. 890 genes were differentially expressed between Sx and ASx samples. Of these, the most significant including genes involved inflammatory signaling (IL16, IL17RA, JAK3, SMPD3, RELT), cell adhesion (OLFML1, CDON, VCAN), and neuromodulation (SEMA6D, ADRA2C, SLC7A5). Gene Set Enrichment Analysis identified functional enrichment in 22 gene ontology (GO) pathways, of which 7 represented immunologic pathways. Weighted Gene Correlation Network Analysis (WGCNA) identified 11 co-expression modules significantly correlated with symptomaticity, including one module strongly enriched for immunologic/inflammatory functions. Of all molecules identified as associated with pain, IL16 expression also correlated with symptom severity when cases were stratified into mild vs. severe pain based on clinical criteria. These findings suggest that endometriotic lesions of symptomatic subjects are characterized by distinct molecular signatures, including altered expression of inflammatory pathways, highlighting potential mechanisms underlying symptom variability and identifying candidate pathways for future therapeutic interventions.
This dataset
Open GSE339400 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 GSE339400_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 PRJNA1498600 and SRA study SRP720019. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 17 sample titles: 17 distinct titles with little repetition. 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
Datasets on a similar topic

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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.