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Multi-omic Sequencing of Intermediate to High-Risk Cutaneous Squamous Cell Carcinoma Identifies Critical Genes and Expression Patterns Associated with Disease and Poor Outcomes

GSE284467 Homo sapiens (human) RNA-seq (gene expression) 75 samples Submitted 2024/12/23 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 75 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
75 samples: 75 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)
Cutaneous squamous cell carcinoma (cSCC) is one of the most common cancers in humans and kills as many people annually as melanoma. The understanding of the transcriptional changes with respect to high-risk clinical/histopathological features and outcome is poor. Here, we examine stage-matched, outcome-differentiated cSCC using whole exome and transcriptome sequencing.
Read the full summary ↓
Exome analysis identified key driver mutations including TP53, CDKN2A, NOTCH1, SHC4, MIIP, CNOT1, C17orf66, LPHN2, and TTC16 and pathway enrichment of driver mutations in replicative senescence, cellular response to UV, cell-cell adhesion, and cell cycle. Transcriptomic analysis identified pathway enrichment of immune signaling/inflammation, cell-cycle pathways, extracellular matrix function, and chromatin function. Integrative analysis identified 183 critical genes in carcinogenesis and were used to develop a gene expression panel (GEP) for outcome. Three outcome-related gene clusters included those involved in keratinization, cell division, and metabolism. We found 16 genes that may predict metastasis (risk score ≥ 9 Met & risk score < 9 NoMet) with an AUC of 97.1%, sensitivity 95.5%, specificity 85.7%, and overall accuracy of 90%. Eleven genes were chosen to generate the risk score for Overall Survival (OS) with an OS prediction of 80.8% and each risk gene increasing the risk of death by 2.47 (HR: 2.47; p<0.001).
This dataset
Open GSE284467 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 GSE284467_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 PRJNA1199334 and SRA study SRP552016. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
75 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 75 sample titles: 75 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

+ 35 more — browse all 75 samples with per-sample file links →

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.