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RNA sequencing of High Grade Serous Ovarian cancer

GSE291288 Homo sapiens (human) RNA-seq (gene expression) 168 samples Submitted 2025/06/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 168 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)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
168 samples: 139 × Brescia Ovarian Tumou vs 29 × Ovarian Tumour Epithelial cellsGroups 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
Oxford Classic-Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment DecisionsClinical cancer research : an official journal of the American Association for Cancer Research · 2026
Summary (written by the submitters)
In this study we have validated the prognostic strength of the Oxford Classic-defined EMT in an independent cohort of HGSOC patients- 'Brescia Ovarian Tumour'. Additionally, we carried out whole transcriptome sequencing of FLOW-sorted tumour epithelial cells from five HGSOC patients at diagnosis.
Published in
Oxford Classic-Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions
Rai L, Ravaggi A, Bignotti E et al. · Clinical cancer research : an official journal of the American Association for Cancer Research 2026 · PMID 40705052 · doi:10.1158/1078-0432.CCR-24-4250
This dataset
Open GSE291288 in finder → Find more human RNA-seq datasets → Ovarian cancer landscape →
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 GSE291288_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 PRJNA1232721 and SRA study SRP568454. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40705052) ↗
Study design
139 × Brescia Ovarian Tumou vs 29 × Ovarian Tumour Epithelial cells

Supports a between-group comparison across 168 samples.

How we read this: 2 replicated groups read from 168 sample titles; they account for 168 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

+ 128 more — browse all 168 samples with per-sample file links →

More Ovarian cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.