GEO series

MECOM promotes leukemia progression and inhibits mast cell differentiation through functional competition with GATA2 (CordBlood_RNA-seq)

GSE324092 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/03/25 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 12 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: 12 samples: 3 × CB, RNA-seq, Empty Vector, vs 3 × CB, RNA-seq, WT, vs 3 × CB, RNA-seq, DL/AS, vs 3 × CB, RNA-seq, R750W,Groups 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: MECOM promotes leukemia progression and inhibits mast cell differentiation through functional competition with GATA2Leukemia · 2026

Summary (written by the submitters)

MECOM is a transcription factor critical for the maintenance of hematopoietic stem cells (HSCs) and the pathogenesis of myeloid leukemia. Germline mutations clustered in the C-terminal zinc finger domain (ZFD) of MECOM are known to cause MECOM-associated syndromes, involving bone marrow failure and skeletal anomalies. However, the molecular consequences of these mutations and the precise downstream mechanisms of MECOM remain elusive.

Read the full summary ↓

Here, we demonstrate that the C-terminal ZFD serves as the dominant DNA-binding module of MECOM, and that disease-associated mutations abrogate its DNA-binding capacity. Mechanistically, we reveal that MECOM functionally antagonizes GATA2 via C-terminal ZFD-mediated DNA binding and recruitment of the corepressor CtBP. This repression promotes myeloid leukemogenesis while suppressing mast cell differentiation. Furthermore, we generated a knockin mouse model harboring a C-terminal ZFD mutation, which successfully recapitulated the clinical phenotypes of MECOM-associated syndromes, including reduction of HSCs and B cells. Collectively, our findings define C-terminal ZFD mutations as loss-of-function mutations with impaired DNA binding, uncover the MECOM-GATA2 axis as a key regulatory pathway, and provide a valuable mouse model for understanding MECOM-associated syndromes.

Published in

MECOM promotes leukemia progression and inhibits mast cell differentiation through functional competition with GATA2

Iida K, Nakanishi M, Nakahara J et al. · Leukemia 2026 · PMID 42086934 · doi:10.1038/s41375-026-02977-4

This dataset

Open GSE324092 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 GSE324092_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 PRJNA1433413 and SRA study SRP681693. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42086934) ↗

Study design

3 × CB, RNA-seq, Empty Vector, vs 3 × CB, RNA-seq, WT, vs 3 × CB, RNA-seq, DL/AS, vs 3 × CB, RNA-seq, R750W,

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

GSM9568104 CB, RNA-seq, Empty Vector, rep1 CB, RNA-seq, Empty Vect…
GSM9568105 CB, RNA-seq, Empty Vector, rep2 CB, RNA-seq, Empty Vect…
GSM9568106 CB, RNA-seq, Empty Vector, rep3 CB, RNA-seq, Empty Vect…
GSM9568107 CB, RNA-seq, WT, rep1 CB, RNA-seq, WT,
GSM9568108 CB, RNA-seq, WT, rep2 CB, RNA-seq, WT,
GSM9568109 CB, RNA-seq, WT, rep3 CB, RNA-seq, WT,
GSM9568110 CB, RNA-seq, DL/AS, rep1 CB, RNA-seq, DL/AS,
GSM9568111 CB, RNA-seq, DL/AS, rep2 CB, RNA-seq, DL/AS,
GSM9568112 CB, RNA-seq, DL/AS, rep3 CB, RNA-seq, DL/AS,
GSM9568113 CB, RNA-seq, R750W, rep1 CB, RNA-seq, R750W,
GSM9568114 CB, RNA-seq, R750W, rep2 CB, RNA-seq, R750W,
GSM9568115 CB, RNA-seq, R750W, rep3 CB, RNA-seq, R750W,

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