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Overcoming intra-tumoral heterogeneity for biomarker discovery in the High-Grade Serous Ovarian Cancer proteome

GSE277107 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2025/06/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 22 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
22 samples: 22 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.
Paper
Overcoming intra-tumoral heterogeneity for biomarker discovery in the high-grade serous ovarian cancer proteomeNPJ precision oncology · 2025
Summary (written by the submitters)
Improved biomarkers of treatment response are needed for patients with High-Grade Serous Ovarian Cancer (HGSC). A challenge is substantial anatomical site-to-site variation in expression. We completed Data Independent Acquisition – Mass Spectrometry (DIA-MS) analysis of over 404 fresh frozen and 78 formalin fixed, paraffin-embedded HGSC tissue samples from ovary (adnexal) and a common secondary site (omentum) in 11 patients.
Read the full summary ↓
This was compared with mutation testing, gene expression and whole genome copy number profiling. Proteins with relatively stable intra-, and variable inter-individual expression (n=1,651), included a 52-protein module reflecting interferon mediated tissue inflammation indicative of a cGAS-STING pathway cytosolic double-stranded (ds) DNA response. The dsDNA sensing / inflammation (DSI) score was higher in omentum compared with ovary. Ovarian HGSC samples showed marked inter-individual differences in inflammatory and immune responses to DNA damage. Stable discriminative features of the HGSC proteome, a prerequisite for clinical predictive biomarkers, are detectable in ovary (adnexal) tissue samples.
Published in
Overcoming intra-tumoral heterogeneity for biomarker discovery in the high-grade serous ovarian cancer proteome
Manda SS, Espersen MM, Mapagu C et al. · NPJ precision oncology 2025 · PMID 40500315 · doi:10.1038/s41698-025-00911-y
This dataset
Open GSE277107 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 GSE277107_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 PRJNA1160805 and SRA study SRP532506. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40500315) ↗
Study design
22 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 22 sample titles: 22 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
More Ovarian cancer datasets

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.