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Sensory neurons drive pancreatic cancer progression through glutamatergic cancer-neuron pseudo-synapses

GSE291933 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/06/01 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
12 samples: 3 × Cancer cells from KPC- Grin2d -/- Mouse N vs 2 × Cancer cells from KPC- Grin2d +/+ Mouse N vs 2 × DRGs from KPC- Grin2d -/- Mouse N vs 2 × DRGs from KPC- Grin2d +/+ Mouse NGroups 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)
The results provide significant insights into the role of Grin2D in regulating the secretion of neurotrophic factors that promote neuritogenesis. Transcriptomic analysis of orthotopically transplanted PDAC cancer cells with a knockout of the NMDA receptor subunit Grin2D, along with dorsal root ganglia (T8–T12) innervating the pancreas, strongly supports this conclusion.
Read the full summary ↓
Furthermore, RNA-Seq analysis of tumor and ganglion biopsies from PDAC patients was performed to validate the identified gene candidates in a human context. This study tested the hypothesis that neuronal glutamate drives pancreatic cancer progression via glutamate-mediated GluN2D signaling at the cancer-neuron pseudo-synapses.
This dataset
Open GSE291933 in finder → Find more mouse 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 GSE291933_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 PRJNA1235799 and SRA study SRP570321. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41005304) ↗
Study design
3 × Cancer cells from KPC- Grin2d -/- Mouse N vs 2 × Cancer cells from KPC- Grin2d +/+ Mouse N vs 2 × DRGs from KPC- Grin2d -/- Mouse N vs 2 × DRGs from KPC- Grin2d +/+ Mouse N

Supports a between-group comparison across 9 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 9 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
Datasets on a similar topic

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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.