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Diagnostic and prognostic biomarkers associated with histotype in advanced epithelial ovarian cancer

GSE295399 Homo sapiens (human) RNA-seq (gene expression) 146 samples Submitted 2025/10/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 146 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
146 samples
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
Diagnostic and prognostic biomarkers associated with histotype in advanced epithelial ovarian cancerScientific reports · 2025
Summary (written by the submitters)
Despite advances in cancer treatments, epithelial ovarian cancer (EOC) remains the leading cause of death among gynecologic cancers. EOC is stratified into five main histopathological subtypes: high-grade serous carcinoma (HGSC), clear cell carcinoma (CCC), mucinous carcinoma (MC), endometrioid carcinoma (EC), and low-grade serous carcinoma (LGSC).
Read the full summary ↓
However, personalized treatment strategies and reliable biomarkers for all histotypes remain elusive. Building on our previous work with early-stage EOC, we aim to explore diagnostic and prognostic biomarkers in advanced-stage EOC, updated to the latest World Health Organization classification guidelines from 2020, using comprehensive transcriptomic profiling from total RNA sequencing of 146 EOC patients. Differential expression analysis identified top 9 histotype-specific gene panels for HGSC, CCC, MC, and EC, including S100A1 (HGSC), ARID3A (CCC), LGALS4 (MC), and SCUBE2 (EC). We also identified gene candidates associated with overall survival and disease-specific survival, reflecting both favorable (e.g., OTOF, EEF1E1-BLOC1S5, and STAC3) and unfavorable (e.g., SMOC1, GDPGP1, EPRS1) clinical outcome. Additionally, enrichment analysis revealed tumor progression-related pathways unique to each histotype, offering insights into molecular mechanisms underlying disease progression and potential therapeutic targets. These findings provide valuable insights into the molecular landscape of advanced-stage EOC, paving the way for more effective diagnostic and prognostic tools across diverse histotypes.
Published in
Diagnostic and prognostic biomarkers associated with histotype in advanced epithelial ovarian cancer
Ittner E, Swenson H, Werner L et al. · Scientific reports 2025 · PMID 41131133 · doi:10.1038/s41598-025-24938-0
This dataset
Open GSE295399 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 GSE295399_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 PRJNA1254600 and SRA study SRP580863. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41131133) ↗
Study design
A cohort of 146 individual samples

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

How we read this: Sample titles run Tumor_EOC_1 … Tumor_EOC_156 and carry no group information.

Samples in this study

+ 106 more — browse all 146 samples with per-sample file links →

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.