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Unique epigenomic signatures identify biologically significant subtypes of MDS and predict response to azacytidine [RNA-Seq]

GSE281562 Homo sapiens (human) RNA-seq (gene expression) 83 samples Submitted 2026/07/27 Platform GPL21290Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 83 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
Myelodysplastic syndromesThis series is part of our curated briefing on it.
Samples
83 samples: 77 × MDS_AZA vs 6 × Healthy_RNAGroups 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)
Myelodysplastic syndromes (MDS) are characterized by genetic mutations affecting epigenetic modifiers and aberrant DNA methylation. Although DNA methyltransferase inhibitors (DMTis) are used to treat MDS, response variability remains a challenge, with limited predictive markers. Through integration of comprehensive genomic, epigenomic, and transcriptomic data, we have gained valuable insights into the intricate interplay between genetic and epigenetic alterations in MDS.
Read the full summary ↓
We identified both hypermethylated and hypomethylated regions in MDS compared to normal, with a significant presence of long-range gene regulatory interactions. Moreover, distinct molecular subtypes enriched for specific mutations were characterized, providing critical insights into MDS biology. Notably, we identified approximately 2,500 differentially methylated regions (DMRs) at baseline that distinguish responders from non-responders to AZA treatment. Leveraging these DMRs and their associated genes within the same topologically associating domains (TADs), we developed robust classifiers integrating DNA methylation (DNAme), gene expression (GE), mutations, Hemoglobin, age, Platelet, Blast BM Blast, and absolute neutrophil count (ANC) data, highlighting the potential of personalized treatment approaches for MDS patients.
This dataset
Open GSE281562 in finder → Find more human RNA-seq datasets → Myelodysplastic syndromes 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 GSE281562_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 PRJNA1184724 and SRA study SRP544454. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
77 × MDS_AZA vs 6 × Healthy_RNA

Supports a between-group comparison across 83 samples.

How we read this: 2 replicated groups read from 83 sample titles; they account for 83 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

+ 43 more — browse all 83 samples with per-sample file links →

More Myelodysplastic syndromes 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.