GEO series

SAGE-Seq gene expression profiles of Hs578T, MCF7, and SUM159PT cells treated with STAT3 or non-targeting siRNAs, DMSO, or CXCR2, PTGIS, HAS1, PFKFB3, JAK, or NQO1 inhibitor

GSE22917 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2011/09/08 Platform GPL9052

At a glance

RNA-seq (gene expression) · human · 27 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: 27 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.

Summary (written by the submitters)

To investigate potential links between Stat3 transcriptional activity and other signaling pathways in breast cancer, we determined the gene expression profiles of three breast cancer cell lines treated with JAK, PTGIS, PFKFB3, CXCR2, HAS1, or NQO1 inhibitor (all of which decreased Stat3 transcriptional activity in Hs 578T cells except for the NQO1 inhibitor), inhibitor treatment vehicle alone (DMSO), STAT3 siRNAs, or non-targeting siRNAs.

Read the full summary ↓

Using the resulting data, we identified a gene signature that was significantly regulated by STAT3 siRNAs and similarly affected by the JAK and at least 3 other inhibitors (or by 4 other inhibitors) but not by the NQO1 inhibitor in Hs 578T cells that was enriched in genes involved in development and correlated with shorter distant metastasis-free survival in primary lymph-node-negative invasive breast tumors. These results emphasize the central importance of Stat3 in CD44+/CD24- stem-cell-like breast cancer cells.

This dataset

Open GSE22917 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 GSE22917_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 PRJNA127973 and SRA study SRP002915. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 21633165) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 27 more — browse all 27 samples with per-sample file links →

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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.