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Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance [RNA-Seq]

GSE319969 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/05/05 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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)
Background: Cutaneous squamous cell carcinoma (cSCC) is among the most common skin cancers and can be associated with poor prognosis in aggressive cases, with a median overall survival of approximately two years. Despite its well-described stepwise progression from actinic keratosis to invasive disease, robust molecular markers that discriminate disease stages and inform clinical decision-making remain limited.
Read the full summary ↓
Objectives: This study aimed to delineate progression continuum and stage-related transcriptional programs underlying cSCC evolution, identify candidate biomarkers for risk stratification and invasion, and assess the broader relevance of these molecular signatures across human cancers. Methods: We applied bulk RNA sequencing (HTG EdgeSeq) and spatial transcriptomics (GeoMx DSP) to profile skin biopsies from 8 patients recruited in collaboration with Bordeaux University Hospital. Importantly, each patient simultaneously presented four stages (healthy skin, pre-malignant lesion, tumor core, and invasive front) providing a unique framework to interrogate the full molecular trajectory of cSCC progression within individuals. Results: Spatial transcriptomic analyses identified more than 2,000 differentially expressed genes (adjusted p < 0.01) whose expression in squamous cells changed across disease stages. These genes were organized into 18 coordinated expression programs reflecting progressive biological rewiring during tumor evolution. Pathways related to cellular stress responses, proliferation, extracellular matrix remodeling, and inflammation were progressively upregulated, whereas differentiation programs and lipid and amino acid metabolism were consistently downregulated. In contrast, peritumoral macrophages showed increased expression of genes involved in purine metabolism, glycolysis, and pyruvate metabolism across progression. To assess the broader clinical relevance of the progression-associated gene programs, we developed a Snakemake-based pipeline to systematically screen 32 solid and hematological malignancies from The Cancer Genome Atlas. This workflow is designed to be fully reproducible and readily extensible, enabling other researchers to perform similar progression and survival analyses across cancer types. Notably, we defined a cSCC-progression signature which significantly correlated with poor overall survival (p < 0.05) in 10 additional cancer types. Furthermore , we last identified 12 cSCC stage-specific biomarkers, whose expression was confirmed to be stage-specific via Visium HD. Conclusions: Together, this study provides a spatially resolved and stage-aware transcriptomic map of cSCC progression, identifies coordinated gene programs underlying disease evolution, and defines progression-associated signatures with prognostic relevance across multiple cancers, highlighting their potential clinical utility.
This dataset
Open GSE319969 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 GSE319969_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 PRJNA1425793 and SRA study SRP678177. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
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.