GEO series

Immunosuppressive myeloid signaling and distinct malignant cell states in pancreatic neuroendocrine tumors revealed by single-nucleus RNA-seq

GSE301075 Homo sapiens (human) Single-nucleus RNA-seq 21 samples Submitted 2026/02/18 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 21 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 21 samples: 17 × pnet vs 2 × pnet_009_MTGroups 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: Distinct Malignant Cell States and Myeloid Glutamate Signaling Associated with Aggressive Pancreatic Neuroendocrine TumorsClinical cancer research : an official journal of the American Association for Cancer Research · 2026

Summary (written by the submitters)

Pancreatic neuroendocrine tumors (PNET) are rare and heterogeneous neoplasms with increasing incidence worldwide. Currently, there is no clinically-relevant molecular framework for risk-stratifying patients with PNET. In this study, we used single-nucleus RNA-sequencing to uncover the spectrum of malignant cell states and interactions within the tumor microenvironment.

Read the full summary ↓

We uncovered a gene expression program enriched in neural/synaptic signaling genes associated with aggressive clinical behavior, altered telomeres, and broad chromosomal loss of heterozygosity. Moreover, it is associated with worse overall survival in independent cohorts. Another novel malignant cell state was enriched for VEGF-signaling and found to interact with macrophages via glutamate, promoting an immunosuppressive phenotype in macrophages and a more invasive phenotype in malignant cells. This study provides insight into the heterogeneity of malignant cells in PNET, and these diverse cell states and intercellular interactions that may be further explored for prognostication and therapeutic targeting.

Published in

Distinct Malignant Cell States and Myeloid Glutamate Signaling Associated with Aggressive Pancreatic Neuroendocrine Tumors

Arbesfeld-Qiu JM, Cho JW, Nguyen PTT et al. · Clinical cancer research : an official journal of the American Association for Cancer Research 2026 · PMID 41849229 · doi:10.1158/1078-0432.CCR-25-2565

This dataset

Open GSE301075 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 GSE301075_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1283221. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41849229) ↗

Study design

17 × pnet vs 2 × pnet_009_MT

Supports a between-group comparison across 19 samples.

How we read this: 2 replicated groups read from 21 sample titles; they account for 19 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

GSM9074710 pnet_2 pnet
GSM9074711 pnet_3 pnet
GSM9074712 pnet_4 pnet
GSM9074713 pnet_6 pnet
GSM9074714 pnet_7 pnet
GSM9074715 pnet_8 pnet
GSM9074716 pnet_010 pnet
GSM9074717 pnet_012 pnet
GSM9074718 pnet_013 pnet
GSM9074719 pnet_014 pnet
GSM9074720 pnet_015 pnet
GSM9074721 pnet_016 pnet
GSM9074722 pnet_017 pnet
GSM9074723 pnet_019 pnet
GSM9074724 pnet_021 pnet
GSM9074725 pnet_022 pnet
GSM9074726 pnet_023 pnet
GSM9074727 MGH_021
GSM9074728 pnet_009_PT1
GSM9074729 pnet_009_MT1 pnet_009_MT
GSM9074730 pnet_009_MT2 pnet_009_MT

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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.