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Roquin-deficient T cells drive pancreatitis and tumorigenesis through IL-17 and Th17-inappropriate Cytokine production

GSE248106 Mus musculus (mouse) RNA-seq (gene expression) 21 samples Submitted 2025/11/30 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 21 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)
Samples
21 samples: 21 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)
Pancreatitis can trigger pancreatic ductal adenocarcinoma (PDAC), however, the underlying origins can be diverse and cellular and molecular mechanisms remain elusive. We show that combined genetic inactivation of the RNA-binding proteins Roquin-1 and Roquin-2 in T cells induced pancreatitis, pancreatic neoplasia and accelerated PDAC formation, if KrasG12D was expressed in acinar cells.
Read the full summary ↓
In the pancreas, Roquin-deficient T cells were highly activated, recruited proinflammatory neutrophils via IL-17A secretion, and inappropriately produced G-CSF that, in a feed-forward loop, further induced and mobilized neutrophils. Consistently, neutralization of IL-17 or G-CSF ameliorated pancreas pathology. Roquin repressed G-CSF in two ways. It directly inhibited CSF3 mRNA expression through its 3'-UTR, and indirectly, because Roquin loss-of-function imposed transcriptional reprogramming by NF-kB and established an active enhancer at the Csf3 locus in Th17 cells. Together, we identified critical cellular and humoral components and epigenetic, transcriptional and post-transcriptional mechanisms within a regulatory circuit that prevents pancreatic cancer formation.
This dataset
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.

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 GSE248106_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1041781 and SRA study SRP472563. Searching any of these in the dataset finder brings you back here.

Study design
21 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 21 sample titles: 21 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
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