← BioTransfer GEO Dataset Finder
GEO series

STAT1 and STAT3 Signaling in Ovarian Cancer: Prognostic Biomarkers and Therapeutic Targets [RNA-seq]

GSE293444 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2026/02/16 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 18 conditions, mostly in triplicateGroups 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)
Epithelial ovarian cancer is the 7th most common cancer in women worldwide and is the most fatal of all female reproductive cancers. JAK/STAT signaling is oncogenic in ovarian cancer and has been reported to be constitutively active in a proportion of chemotherapy resistant ovarian cancer cell lines and tumors. However, the precise molecular mechanisms by which STAT1 and STAT3 function in ovarian cancer are not well understood and the consequences of their specific inhibition have yet to be determined.
Read the full summary ↓
Methods: To delineate the specific functions of STAT1 and STAT3 in high grade serous and endometrioid ovarian cancer cell lines, we treated OVSAHO and MDAH cells with IFNγ or IL6, canonical STAT1 and STAT3 activators respectively, with and without STAT1 or STAT3 specific siRNAs. We conducted RNA-sequencing and ChIP-sequencing following treatment to delineate direct and indirect target genes for STAT1 and STAT3. We generated a STAT1 and STAT3 specific gene signature and applied them to publicly available datasets to test their predictive and prognostic ability. Results: Using these global and unbiased datasets, we identified STAT1 and STAT3 specific pathways, developed STAT1 and STAT3 specific gene signatures, and applied these signatures to publicly available ovarian cancer patient data. We determined the STAT1 and STAT3 signature’s associations with tumor development, chemotherapy responsiveness and long-term patient outcomes. Conclusions: Our studies have further informed the precise mechanisms of STAT1 and STAT3 action in the two most common subtypes of ovarian cancer and have defined gene signatures that are both prognostic and predictive.
This dataset
Open GSE293444 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 GSE293444_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 PRJNA1244634 and SRA study SRP575784. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 conditions, mostly in triplicate
MDAH, Control siRNA + IL6, ×3 MDAH, Control siRNA + Veh, ×3 MDAH, Control siRNA + IFNγ, ×3 MDAH, STAT1 siRNA + IL6, ×3 MDAH, STAT1 siRNA + Veh, ×3 MDAH, STAT1 siRNA + IFNγ, ×3 MDAH, STAT3 siRNA + IL6, ×3 MDAH, STAT3 siRNA + Veh, ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 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.