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STAT6 mutations compensate for CREBBP mutations and hyperactivate IL4/STAT6/RRAGD/mTOR signaling in follicular lymphoma

GSE261465 Homo sapiens (human) RNA-seq (gene expression) 46 samples Submitted 2025/03/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 46 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
Follicular lymphomaThis series is part of our curated briefing on it.
Samples
46 samples: 46 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.
Paper
STAT6 mutations compensate for CREBBP mutations and hyperactivate IL4/STAT6/RRAGD/mTOR signaling in follicular lymphomaLeukemia · 2025
Summary (written by the submitters)
Activating mutations in STAT6 are common in Follicular Lymphoma (FL) and transformed FL and various other B cell lymphomas. Here, we report RNA-seq based gene expression data on normal human lymph node derived B lymphocytes (NBC; N = 6), and primary human FL WT (N = 11) or mutant (N = 4) for STAT6 before and after ex vivo stimulation with IL4.
Read the full summary ↓
We found that STAT6 mutants result in broad based augmentation of IL4-induced gene expression. Unexpectedly, in FL with WT STAT6 we measured reduced baseline and IL4-induced gene expression levels when compared with NBC lymphocytes or FL with STAT6 mutations. We tracked the attenuated IL4/JAK/STAT6 response to co-existing CREBBP mutations and experimentally verified that intact CREBBP is required for the induction of many IL4-induced genes. One of the IL4-induced genes here identified is RRAGD, a small G-protein involved in lysosomal mTOR activation. We show that IL4 treatment induced RRAGD expression, that RRAGD is required for mTOR activation in lymphoma cells and that IL4-enhanced BCR signaling induced mTOR activation. The IL4 and BCR-induced mTOR activation was reduced by CREBBP mutants and augmented by mutant STAT6, establishing a link between STAT6 mutations and mTOR regulated pro-growth pathways in lymphoma.
Published in
STAT6 mutations compensate for CREBBP mutations and hyperactivate IL4/STAT6/RRAGD/mTOR signaling in follicular lymphoma
Shao Q, Bedi K, Malek IA et al. · Leukemia 2025 · PMID 39910284 · doi:10.1038/s41375-025-02525-6
This dataset
Open GSE261465 in finder → Find more human RNA-seq datasets → Follicular lymphoma 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 GSE261465_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 PRJNA1087078 and SRA study SRP494913. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39910284) ↗
Study design
46 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 5 samples read as cases, 30 as controls.

How we read this: Read from 46 sample titles: 46 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

+ 6 more — browse all 46 samples with per-sample file links →

More Follicular lymphoma 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.