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DNA methylation and transcriptomic profiles of Wilms tumour reveal new deregulated genes and epigenetic processes relevant for tumour stratification and management

GSE303967 Homo sapiens (human) DNA methylation 45 samples Submitted 2026/06/23 Platform GPL24676
At a glance

DNA methylation · human · 45 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Topic
Wilms tumourThis series is part of our curated briefing on it.
Samples
45 samples: 23 × DNA from Non neoplastic kidney control vs 22 × DNA from Wilms TumoGroups 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)
Wilms tumour (WT), the most common kidney cancer in children, presents features of altered kidney development and frequently exhibits molecular alterations at the 11p15.5 imprinted locus, affecting the IGF2 and H19 genes, which contribute to tumour growth and may also act as predisposing events that require additional hits for tumour initiation.
Read the full summary ↓
The epigenetic landscape beyond 11p15.5 suggests diagnostic and prognostic potential, but its link to transcriptomic changes is largely unexplored. We integrated methylomic and transcriptomic datasets of 27 primary tumours and matched non-neoplastic kidneys. DNA methylation profiling identified around 9,000 differentially methylated CpG sites distinguishing neoplastic from non-neoplastic tissue and other pediatric cancers, thus representing a novel WT-specific epigenetic signature. We found that cases with Imprinting Centre 1 (IC1) gain of methylation exhibited the most extensive epigenetic alterations, LOH tumours showed intermediate changes, whereas regressive tumours with largely normal 11p15.5 status displayed limited methylation abnormalities. Three methylation clusters corresponding to transcriptomic subtypes were identified, characterised by distinct tumour microenvironment and chemosensitivity predictions: a regressive-enriched group with high immune infiltration and predicted susceptibility to paclitaxel, a proliferative group with high tumour purity and predicted sensitivity to doxorubicin, and a stromal-like group with intermediate predicted sensitivity to both drugs. Combined analysis of methylation and expression data revealed more than 900 genes under epigenetic control, which contributed to defining the WT subtypes. Analysis of the IGF2/H19 locus uncovered multiple regulatory mechanisms underlying IGF2 activation, including imprinting defects at IC1, methylation changes at DMR0, differential promoter usage, and transcriptional modulation by PLAG1 and BAHD1. These findings define the epigenetic architecture of WT, encompassing genome-wide methylation changes, subtype-specific patterns, and their integration with gene expression, supporting stratification of cases for improved prognosis and therapeutic approaches.
This dataset
Open GSE303967 in finder → Find more human RNA-seq datasets → Wilms tumour 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 GSE303967_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1298322 and SRA study SRP604396. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 × DNA from Non neoplastic kidney control vs 22 × DNA from Wilms Tumo

Supports a between-group comparison across 45 samples.

How we read this: 2 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 samples with per-sample file links →

More Wilms tumour datasets

Search all human RNA-seq datasets in GEO →

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.