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An EGFR Co-amplified Long Noncoding RNA HELDR Promotes Glioblastoma Malignancy through KAT7-driven gene programs [ChIRP]

GSE286432 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 21 samples Submitted 2026/02/07 Platform GPL34295
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 21 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
GlioblastomaThis series is part of our curated briefing on it.
Samples
21 samples: 21 samples, each with a distinct title — no replicated groupsGroups 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
An EGFR co-amplified lncRNA HELDR promotes glioblastoma malignancy through KAT7-driven gene programsNature cell biology · 2026
Summary (written by the submitters)
EGFR amplification at extrachromosome DNAs (ecDNAs) is a major driving mutation in glioblastoma (GBM). However, targeting EGFR for GBM treatments remain unsatisfied. Here we identify a de novo long non-coding RNA (lncRNA) that is co-amplified with EGFR which we named hidden EGFR long non-coding downstream RNA (HELDR). HELDR is a GBM-specific lncRNA that promotes tumorigenicity independent of EGFR expression and signaling.
Read the full summary ↓
HELDR globally binds genomic DNA and recruits a transcription co-activator p300 to the promoter of KAT7. p300-induced H3K27ac at KAT7 promoter enlists other co-transcription factors, activating KAT7 transcription. KAT7 induces H3K14ac H4K12ac that activate the transcription of KAT7-targeting gene programs critical for GBM malignancy. Targeting KAT7 synergistically enhances therapeutic effects of targeting EGFR for GBM treatment. These results not only reveal the role of HELDR in EGFR-driven GBM malignancy, but also provide strong rationale to characterize the roles of lncRNAs co-amplified with driver oncogenes at ecDNAs in human cancers.
Published in
An EGFR co-amplified lncRNA HELDR promotes glioblastoma malignancy through KAT7-driven gene programs
Yu X, Song X, Schäfer RA et al. · Nature cell biology 2026 · PMID 41896311 · doi:10.1038/s41556-026-01924-w
This dataset
Open GSE286432 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Glioblastoma 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 GSE286432_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1209136 and SRA study SRP556609. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41896311) ↗
Study design
21 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 21 sample titles: 21 distinct titles with little repetition. 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
More Glioblastoma datasets

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