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RNA N6-Methyladenosine (m6A) regulates cell cycle progression in Diffuse Midline Glioma (DMG) and confers sensitivity to FTO inhibition

GSE279022 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/03/24 Platform GPL24676Platform GPL26167Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 30 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)
Samples
30 samples: 11 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
RNA N6-methyladenosine (m6A) regulates cell cycle progression in diffuse midline glioma (DMG) and confers sensitivity to FTO inhibitionCell death & disease · 2026
Summary (written by the submitters)
The epitranscriptome has not previously been evaluated in DMG. Given the pivotal role of m6A in normal OPC biology, investigating its dysregulation in DMG could uncover new therapeutic options and improve our understanding of the disease's underlying mechanisms. Here, for the first time, we perform direct RNA-sequencing on patient-derived DMG cultures to generate transcript and base-resolution m6A landscapes of DMG. Furthermore, we interrogate the therapeutic benefits of the Fat mass and obesity associated (FTO) player inhibitor FB23-2 in DMG using direct RNA sequencing and RNA sequencing
Published in
RNA N6-methyladenosine (m6A) regulates cell cycle progression in diffuse midline glioma (DMG) and confers sensitivity to FTO inhibition
Ross SE, Holliday H, Wang E et al. · Cell death & disease 2026 · PMID 41888518 · doi:10.1038/s41419-026-08647-8
This dataset
Open GSE279022 in finder → Find more human RNA-seq datasets →
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 GSE279022_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: 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 PRJEB80588 and SRA study ERP164559. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41888518) ↗
Study design
11 conditions, mostly in triplicate
SU-DIPGXIII_DMSO_RNAseq ×3 SU-DIPGXIII_FB232_RNAseq ×3 SU-DIPGXVII_DMSO_RNAseq ×3 SU-DIPGXVII_FB232_RNAseq ×3 HSJD-DIP007_DMSO_RNAseq ×3 HSJD-DIP007_FB232_RNAseq ×3 SU-DIPGXIII_RNA002 ×2 SU-DIPGXVII_DMSO_RNA004 ×2 +5 more

Supports a between-group comparison across 28 samples.

How we read this: 11 replicated groups read from 30 sample titles; they account for 28 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
Datasets on a similar topic

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