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The role of EHMT2 in Uveal melanoma progression [EHMT2_Inhibitors]

GSE196624 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/11/14 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 18 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
Uveal melanomaThis series is part of our curated briefing on it.
Samples
18 samples
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
Loss of EHMT2 enhances NK cell-driven anti-tumor immunity through TGF-β1 suppressionEMBO molecular medicine · 2026
Summary (written by the submitters)
EHMT2/G9a expression is elevated in many cancer types including uveal melanoma. Here in the present study, we have identified pharmacological inhibition of EHMT2/G9a in MP41 cells and Mel92.1 cells promote the sensitivity to NK cells in vitro. By keeping this in mind, we have performed RNA-seq of inhibition of EHMT2/G9a validated cells to identify the differentially regulated genes from various pathways that are responsible for promoting resistance to NK cell mediated cytotoxicity.
Published in
Loss of EHMT2 enhances NK cell-driven anti-tumor immunity through TGF-β1 suppression
Chava S, Bugide S, Malvi P et al. · EMBO molecular medicine 2026 · PMID 41366520 · doi:10.1038/s44321-025-00357-6
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 GSE196624_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 PRJNA806658 and SRA study SRP359641. Searching any of these in the dataset finder brings you back here.

Samples in this study

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