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Lean breast adipocytes secrete an oxylipin that suppresses breast cancer via ferroptosis

GSE300839 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2025/08/20 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
Mus musculus (mouse)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
30 samples: 30 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.
Summary (written by the submitters)
Obesity is predicted to become the largest modifiable risk factor for breast cancer in postmenopausal women, yet the mechanisms underlying this association are unclear. We identified a novel protective mechanism for lean adipocytes to suppress breast cancer by secreting the oxylipin 9S-HODE, which induces ferroptosis in breast cancer cells while sparing normal breast epithelial cells.
Read the full summary ↓
Consequently, the inhibition of ferroptosis accelerates breast cancer in lean, but not obese, mice. Obese adipocytes fail to secrete 9S-HODE, suggesting that the loss of 9S-HODE significantly contributes to the acceleration of breast cancer in obesity. Further, 9S-HODE supplementation into tumors in obese mice is sufficient to reduce tumor burden and additionally can inhibit the growth of patient-derived breast cancer organoids, underscoring its potential as a therapeutic agent.
This dataset
Open GSE300839 in finder → Find more RNA-seq datasets → Breast 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 GSE300839_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 PRJNA1282180 and SRA study SRP594672. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42721252) ↗
Study design
30 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 30 sample titles: 30 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 Breast cancer datasets

Search all 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.