GEO series

Integrative analysis of mRNA stability regulation uncovers a metastasis-suppressive program in breast cancer [BRCA_SLAM-seq]

GSE301426 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/09/03 Platform GPL20301

At a glance

RNA-seq (gene expression) · human · 12 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: 12 samples: 6 conditions, mostly in duplicateGroups 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: Integrative analysis of mRNA stability regulation uncovers a metastasis-suppressive program in breast cancerScience advances · 2026

Summary (written by the submitters)

Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alterations, yet post-transcriptional regulation likely influences these patterns as well. However, the quantitative contribution of post-transcriptional mechanisms to cancer transcriptome dynamics remains unclear. Here, we systematically measured mRNA dynamics across diverse breast cancer models, revealing that mRNA stability significantly shapes gene expression variability.

Read the full summary ↓

To decipher the regulatory grammar underlying these dynamics, we developed GreyHound, an interpretable multimodal deep-learning framework integrating RNA sequence features and RNA-binding protein (RBP) expression. GreyHound identified an extensive network of RBPs and their regulons underlying variations in mRNA stability. Among these, we uncovered a metastasis-suppressive regulatory axis centered on the RNA-binding protein RBMS3 and its post-transcriptional control of the redox regulator TXNIP. Functional and molecular analyses revealed that RBMS3 depletion resulted in targeted transcript destabilization, which was associated with poor clinical outcomes and enhanced metastatic potential in xenograft models. Using in vivo epistasis studies, we confirmed that RBMS3 regulation of TXNIP mRNA stability drives this metastasis-suppressive program. These findings position the RBMS3-TXNIP regulatory axis as a key post-transcriptional mechanism in breast cancer and illustrate how interpretable models of RNA dynamics can uncover hidden regulatory programs in disease.

Published in

Integrative analysis of mRNA stability regulation uncovers a metastasis-suppressive program in breast cancer

Karner H, Mittmann TC, Chen VW et al. · Science advances 2026 · PMID 41811965 · doi:10.1126/sciadv.aea9061

This dataset

Open GSE301426 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 GSE301426_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 PRJNA1285169 and SRA study SRP597038. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41811965) ↗

Study design

6 conditions, mostly in duplicate

HCC1806 cells, biol ×2 HCC38 cells, biol ×2 MCF7 cells, biol ×2 MDA-MB-231 cells, biol ×2 MDA-MB-453 cells, biol ×2 ZR-75-1 cells, biol ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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

GSM9083149 HCC1806 cells, biol rep 1 HCC1806 cells, biol
GSM9083150 HCC1806 cells, biol rep 2 HCC1806 cells, biol
GSM9083151 HCC38 cells, biol rep 1 HCC38 cells, biol
GSM9083152 HCC38 cells, biol rep 2 HCC38 cells, biol
GSM9083153 MCF7 cells, biol rep 1 MCF7 cells, biol
GSM9083154 MCF7 cells, biol rep 2 MCF7 cells, biol
GSM9083155 MDA-MB-231 cells, biol rep 1 MDA-MB-231 cells, biol
GSM9083156 MDA-MB-231 cells, biol rep 2 MDA-MB-231 cells, biol
GSM9083157 MDA-MB-453 cells, biol rep 1 MDA-MB-453 cells, biol
GSM9083158 MDA-MB-453 cells, biol rep 2 MDA-MB-453 cells, biol
GSM9083159 ZR-75-1 cells, biol rep 1 ZR-75-1 cells, biol
GSM9083160 ZR-75-1 cells, biol rep 2 ZR-75-1 cells, biol

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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.