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Characterization of a Human Cutaneous Squamous Cell Carcinoma Xenograft Mouse Model

GSE326341 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2026/06/05 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 20 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
Cutaneous squamous cell carcinomaThis series is part of our curated briefing on it.
Samples
20 samples: 5 × HD, skin, vs 5 × xSCC, tumor xenograft, vs 5 × ES, human engineered skin, xenograft, vs 5 × huSCC, human cutaneous squamous cell carcinoma,Groups 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
Harnessing skin-resident γδ T cells for immunotherapy in cutaneous squamous cell carcinomaScience advances · 2026
Summary (written by the submitters)
To study the biology of immune cell biology (e.g. human cutaneous gamma delta T cell) in human skin tumours, an in vivo SCC xenograft mouse model was established. To closely resemble the human skin microenvironment, human tumour cells were introduced into an engineered human skin on the back of NSG mice. Transcriptome analysis of bulk-RNAseq data generated from biopsies of the xenograft model as well as human skin and tumour was conducted to test which features of the model resemble the situation in humans.
Published in
Harnessing skin-resident γδ T cells for immunotherapy in cutaneous squamous cell carcinoma
Nasi G, Schöftner LC, Ronacher L et al. · Science advances 2026 · PMID 42341104 · doi:10.1126/sciadv.aec7215
This dataset
Open GSE326341 in finder → Find more human RNA-seq datasets → Cutaneous squamous cell carcinoma 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 GSE326341_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 PRJNA1445120 and SRA study SRP687727. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42341104) ↗
Study design
5 × HD, skin, vs 5 × xSCC, tumor xenograft, vs 5 × ES, human engineered skin, xenograft, vs 5 × huSCC, human cutaneous squamous cell carcinoma,

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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
More Cutaneous squamous cell carcinoma 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.