GEO series

ZNF180 modulates tumor intrinsic immunotherapy resistance in melanoma through driving plasticity [RNA-Seq]

GSE307748 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/09/30 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: MelanomaThis series is part of our curated briefing on it.
Samples: 12 samples: 6 × 24132XR-01 vs 6 × 24132XR-06Groups 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: ZNF180 modulates tumor intrinsic immunotherapy resistance in melanoma through driving plasticitybioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Through analyzing publicly available single-cell and bulk sequencing data from ICI-treated cohorts, ZNF180-regulome was predictive of ICI responses in independent bulk sequencing cohorts, and ZNF180+ tumors persisted after the therapy with immune-suppressive features such as MHC-I loss and CD155 expressions, the primary ligand to TIGIT inhibitory receptor.

Read the full summary ↓

To investigate regulatory roles of ZNF180 to confer these immune suppressive phenotypes, we performed ZNF180 knock-down in melanoma cells in vitro with different genetic backgrounds, namely A375 (BRAF-mutant) and SKMEL147 (NRAS-mutant) cells, and performed RNA- and ATAC-sequencing. The integrative analysis revealed ZNF180 silencing promoted tumor immunogenicity through gain of accessibility on MHC-I/-II coding genes, CD155 down-regulation to avoid TIGIT/CD155 checkpoint signaling, and suppressed AP-1 transcription factor activities as the drivers of melanoma reprogramming towards MITFlowAXLhigh de-differentiated cells. Further, ZNF180 silencing increased CD4 helper T-cell infiltrations in tumors and regressed the tumors in vivo. Collectively, these results indicate ZNF180 is a tumor intrinsic regulator of melanoma plasticity to drive de-differentiated phenotypes with immune-suppressive features including loss of immunogenicity, T-cell inhibitory signals through TIGIT/CD155 checkpoint and exclusion of CD4 helper T-cells. As ZNF180-regulome manifests in non-metastatic melanoma in contrast to the current focus of standard-of-care ICI on the metastatic disease, these results establish ZNF180-regulome as a biomarker and novel therapeutic avenue for early stage melanoma to intervene ICI resistance.

Published in

ZNF180 modulates tumor intrinsic immunotherapy resistance in melanoma through driving plasticity

Song WM, Kondapuram SK, Zhou X et al. · bioRxiv : the preprint server for biology 2025 · PMID 41000822 · doi:10.1101/2025.09.16.676573

This dataset

Open GSE307748 in finder → Find more human RNA-seq datasets → Melanoma 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 GSE307748_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1327258 and SRA study SRP619073. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41000822) ↗

Study design

6 × 24132XR-01 vs 6 × 24132XR-06

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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

GSM9230292 24132XR-01-01 24132XR-01
GSM9230293 24132XR-01-02 24132XR-01
GSM9230294 24132XR-01-03 24132XR-01
GSM9230295 24132XR-01-04 24132XR-01
GSM9230296 24132XR-01-05 24132XR-01
GSM9230297 24132XR-01-06 24132XR-01
GSM9230298 24132XR-06-01 24132XR-06
GSM9230299 24132XR-06-02 24132XR-06
GSM9230300 24132XR-06-03 24132XR-06
GSM9230301 24132XR-06-04 24132XR-06
GSM9230302 24132XR-06-05 24132XR-06
GSM9230303 24132XR-06-06 24132XR-06

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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.