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Melanoma-derived exosomal miRNAs and keratinocyte-derived LL-37 define an epidermal–tumor arms race in melanoma

GSE334049 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/07/22 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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)
Topic
MelanomaThis series is part of our curated briefing on it.
Samples
10 samples: 3 × Ctrl vs 3 × LL-37 vs 2 × cko vs 2 × WTGroups 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)
Melanoma progression involves dynamic interactions between tumor cells and the surrounding epidermal microenvironment. The cathelicidin antimicrobial peptide LL-37, expressed by keratinocytes, has been implicated in tumor-host defense; however, its transcriptomic impact on melanoma cells and its in vivo relevance remain incompletely characterized.
Read the full summary ↓
Here, we performed bulk RNA sequencing of LL-37-treated A375 human melanoma cells in vitro and of tumor-bearing dorsal skin from K14CreCampf/f (cKO) and wild type mice (WT) subjected to topical imiquimod stimulation in vivo, to elucidate the transcriptomic landscape underlying LL-37-mediated suppression of melanoma progression.
This dataset
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.

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 GSE334049_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1473650 and SRA study SRP706047. Searching any of these in the dataset finder brings you back here.

Study design
3 × Ctrl vs 3 × LL-37 vs 2 × cko vs 2 × WT

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 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
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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.