← BioTransfer GEO Dataset Finder
GEO series

The GLP-1R agonist semaglutide reshapes pancreatic cancer associated fibroblasts reducing collagen hydroxyprolination and favoring immune infiltration.

GSE266899 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/01/29 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 18 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
The GLP-1R agonist semaglutide reshapes pancreatic cancer associated fibroblasts reducing collagen proline hydroxylation and favoring T lymphocyte infiltrationJournal of experimental & clinical cancer research : CR · 2025
Summary (written by the submitters)
Metabolic syndrome represents a risk factor of pancreatic ductal adenocarcinoma (PDAC), the gastrointestinal cancer with the lowest survival rate and therapeutic outcome. PDAC results bidirectional interconnected with metabolic alteration, which favors its onset and occurs during early phases of its development. Pancreatic neoplastic lesions evolve within a highly dense desmoplastic stroma, characterized by abundant extracellular matrix deposition consequence of a prominent cancer associated fibroblast (CAF) activation.
Read the full summary ↓
Here, the complex association between dysmetabolism and PDAC has been analyzed with a specific focus on CAFs to unveil novel targets exploitable for diagnostic and therapeutic purposes. Methods: PDAC development upon dysmetabolic conditions was investigated in: 1) wild type immunocompetent syngeneic mice by orthotopic transplantation of PanIN-bearing Pdx1-Cre; Kras+/LSL-G12D; Trp53+/LSL-R172H organoids (KPC mice) fed with high fat diet to recapitulate dysmetabolism-dependent alterations; and 2) primary pancreatic CAFs isolated from chemotherapy naïve PDAC patients with or without an history of metabolic syndrome. Results: The dysmetabolic-associated higher PDAC aggressiveness was paralleled by an enrichment of collagen fibrils due to an increased function of the prolyl 4-hydroxylase subunit alpha 1 (P4HA1). Upon dysmetabolic conditions, P4HA1 boosts collagen hydroxy-prolination, intensifying collagen contraction strength and thus preventing CD3 positive lymphocyte PDAC infiltration. Interestingly, semaglutide, an incretin agonist with anti-diabetic and anti-obesity properties, is able to prevent the higher stromal deposition of PDAC observed in dysmetabolic conditions, preventing CAF contraction, allowing immune infiltration and reducing tumor development. Conclusion: These results shed light on novel therapeutic options for PDAC patients affected by metabolic syndrome aimed at pancreatic cancer stroma reshape
Published in
The GLP-1R agonist semaglutide reshapes pancreatic cancer associated fibroblasts reducing collagen proline hydroxylation and favoring T lymphocyte infiltration
Cencioni C, Malatesta S, Vigiano Benedetti V et al. · Journal of experimental & clinical cancer research : CR 2025 · PMID 39828692 · doi:10.1186/s13046-024-03263-w
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 GSE266899_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 PRJNA1108693 and SRA study SRP506219. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.