GEO series
A targetable developmental program co-regulates angiogenesis and immune evasion [RNA-Seq]
GSE311908
Mus musculus (mouse)
RNA-seq (gene expression)
42 samples
Submitted 2025/12/03
Platform GPL34281Platform GPL34328
At a glance
RNA-seq (gene expression) · mouse · 42 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
- 42 samples: 14 conditions, mostly in triplicateGroups 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
- A Targetable Developmental Program Coregulates Angiogenesis and Immune Evasion in MelanomaCancer discovery · 2026
Summary (written by the submitters)
Ultraviolet (UV)-induced DNA mutations produce genetic drivers of cutaneous melanoma initiation and numerous neoantigens that can trigger anti-tumor immune responses in the host. Consequently, melanoma cells must rapidly evolve to evade immune detection by simultaneously modulating cell-autonomous epigenetic mechanisms and tumor-microenvironment interactions.
Read the full summary ↓
Angiogenesis has been implicated in this process; although an increase of vasculature facilitates the immune response in normal tissue, solid tumors manage to somehow enhance blood flow while preventing immune cell infiltration. By comparing the expression of transcription factors (TFs) across early-stage melanoma, naevi, and other cancer types, we found the homeodomain-containing TF HOXD13 drives a melanoblast-like developmental program, which is upregulated in melanoma and strongly correlated with angiogenesis and immune cell exclusion. Using transcriptomics, 3D chromatin profiling, and in vivo models, we demonstrate that HOXD13 upregulation promotes tumor growth in vivo by concomitantly enhancing angiogenesis and suppressing T-cell infiltration. HOXD13 orchestrates 3D chromatin contacts between distal enhancers and promoters, simultaneously activating VEGFA, SEMA3A, and CD73. VEGFA and SEMA3A remodel the tumor vasculature and CD73 elevates extracellular levels of adenosine, a vasodilator and immune suppressor that binds adenosine receptors (AdR) on endothelial and T cells. In line with these findings, HOXD13-induced growth advantage in vivo was significantly reversed by the concomitant administration of VEGFR and AdR inhibitors. By revealing a dual pro-angiogenic and immunosuppressive HOXD13-CD73/VEGF gene regulatory axis, we identify a subset of patients who might benefit from combinations of AdR and VEGFR inhibitors which are currently being tested in clinical trials.
Published in
Berico P, Flores Yanke A, Vand-Rajabpour F et al.
· Cancer discovery 2026
· PMID 41631877
· doi:10.1158/2159-8290.CD-24-1853
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 GSE311908_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 PRJNA1371916 and SRA study SRP650176. Searching any of these in the dataset finder brings you back here.
Study design
14 conditions, mostly in triplicate
A375 shNTC RNA-seq ×3
A375 shHOXD13-2 RNA-seq ×3
A375 shHOXD13-3 RNA-seq ×3
A375 GFP RNA-seq ×3
A375 HOXD13 RNA-seq ×3
SKMEL-5 shNTC RNA-seq ×3
SKMEL-5 shHOXD13-2 RNA-seq ×3
SKMEL-5 shHOXD13-3 RNA-seq ×3
+7 more
Supports a between-group comparison across 41 samples.
How we read this: 14 replicated groups read from 42 sample titles; they account for 41 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
- GSM9334342 A375 shNTC RNA-seq rep1 A375 shNTC RNA-seq
- GSM9334343 A375 shNTC RNA-seq rep2 A375 shNTC RNA-seq
- GSM9334344 A375 shNTC RNA-seq rep3 A375 shNTC RNA-seq
- GSM9334345 A375 shHOXD13-2 RNA-seq rep1 A375 shHOXD13-2 RNA-seq
- GSM9334346 A375 shHOXD13-2 RNA-seq rep2 A375 shHOXD13-2 RNA-seq
- GSM9334347 A375 shHOXD13-2 RNA-seq rep3 A375 shHOXD13-2 RNA-seq
- GSM9334348 A375 shHOXD13-3 RNA-seq rep1 A375 shHOXD13-3 RNA-seq
- GSM9334349 A375 shHOXD13-3 RNA-seq rep2 A375 shHOXD13-3 RNA-seq
- GSM9334350 A375 shHOXD13-3 RNA-seq rep3 A375 shHOXD13-3 RNA-seq
- GSM9334351 A375 GFP RNA-seq rep1 A375 GFP RNA-seq
- GSM9334352 A375 GFP RNA-seq rep2 A375 GFP RNA-seq
- GSM9334353 A375 GFP RNA-seq rep3 A375 GFP RNA-seq
- GSM9334354 A375 HOXD13 RNA-seq rep1 A375 HOXD13 RNA-seq
- GSM9334355 A375 HOXD13 RNA-seq rep2 A375 HOXD13 RNA-seq
- GSM9334356 A375 HOXD13 RNA-seq rep3 A375 HOXD13 RNA-seq
- GSM9334357 SKMEL-5 shNTC RNA-seq rep1 SKMEL-5 shNTC RNA-seq
- GSM9334358 SKMEL-5 shNTC RNA-seq rep2 SKMEL-5 shNTC RNA-seq
- GSM9334359 SKMEL-5 shNTC RNA-seq rep3 SKMEL-5 shNTC RNA-seq
- GSM9334360 SKMEL-5 shHOXD13-2 RNA-seq rep1 SKMEL-5 shHOXD13-2 RNA-…
- GSM9334361 SKMEL-5 shHOXD13-2 RNA-seq rep2 SKMEL-5 shHOXD13-2 RNA-…
- GSM9334362 SKMEL-5 shHOXD13-2 RNA-seq rep3 SKMEL-5 shHOXD13-2 RNA-…
- GSM9334363 SKMEL-5 shHOXD13-3 RNA-seq rep1 SKMEL-5 shHOXD13-3 RNA-…
- GSM9334364 SKMEL-5 shHOXD13-3 RNA-seq rep2 SKMEL-5 shHOXD13-3 RNA-…
- GSM9334365 SKMEL-5 shHOXD13-3 RNA-seq rep3 SKMEL-5 shHOXD13-3 RNA-…
- GSM9334366 501mel
- GSM9334367 SKMEL-5 SKMEL
- GSM9334368 SKMEL-147 SKMEL
- GSM9334369 HCmel1274 shNTC RNA-seq rep1 HCmel1274 shNTC RNA-seq
- GSM9334370 HCmel1274 shNTC RNA-seq rep2 HCmel1274 shNTC RNA-seq
- GSM9334371 HCmel1274 shNTC RNA-seq rep3 HCmel1274 shNTC RNA-seq
- GSM9334372 HCmel1274 shHoxd13-3 RNA-seq rep1 HCmel1274 shHoxd13-3 RN…
- GSM9334373 HCmel1274 shHoxd13-3 RNA-seq rep2 HCmel1274 shHoxd13-3 RN…
- GSM9334374 HCmel1274 shHoxd13-3 RNA-seq rep3 HCmel1274 shHoxd13-3 RN…
- GSM9334375 HCmel1274 shHoxd13-4 RNA-seq rep1 HCmel1274 shHoxd13-4 RN…
- GSM9334376 HCmel1274 shHoxd13-4 RNA-seq rep2 HCmel1274 shHoxd13-4 RN…
- GSM9334377 HCmel1274 shHoxd13-4 RNA-seq rep3 HCmel1274 shHoxd13-4 RN…
- GSM9334378 YUMM1.7 EMPTY RNA-seq rep1 YUMM1.7 EMPTY RNA-seq
- GSM9334379 YUMM1.7 EMPTY RNA-seq rep2 YUMM1.7 EMPTY RNA-seq
- GSM9334380 YUMM1.7 EMPTY RNA-seq rep3 YUMM1.7 EMPTY RNA-seq
- GSM9334381 YUMM1.7 HOXD13 RNA-seq rep1 YUMM1.7 HOXD13 RNA-seq
+ 2 more — browse all 42 samples with per-sample file links →
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Share this dataset
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.