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The m6A methyltransferase METTL14 promotes oncogenic Kras induced juvenile myelomonocytic leukemia through dysregulating autophagy

GSE298134 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/01/21 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
12 samples: 12 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
The m6A methyltransferase METTL14 promotes oncogenic Kras induced juvenile myelomonocytic leukemia through dysregulating autophagyCell death and differentiation · 2026
Summary (written by the submitters)
The M6A modification plays an important role in the pathogenesis of various myeloid malignancies. However, its specific role in RAS mutant-induced myeloid malignancy is incompletely understood. In this study, we found that m6A methyltransferase METTL14 was highly expressed and associated with a shorter survival in a RAS-mutant myeloid malignancy, juvenile myelomonocytic leukemia (JMML).
Read the full summary ↓
The knockout of METTL14 was revealed to significantly promote hematopoietic stem/progenitor cells (HSPCs) expansion and suppresses disease progression in a KrasG12D/+ mutant-induced mouse model of JMML. Moreover, knockout of METTL14 reduces hyperproliferation of KrasG12D/+ HSPCs and suppresses oncogenic KrasG12D/+-induced myeloid disease in a cell-autonomous manner. Mechanistically, we revealed that the knockout of METTL14 reduced the autophagy levels of HSPCs by suppressing the transcription and translation of autophagy-related genes, such as Atg5 and Atg9a, through m6A modification. Furthermore, we found that the autophagy inhibition through knockout of ATG5 in Kras mutant mice promoted the expansion of HSPCs and inhibited the progression of leukemia disease, consistent with the phenotypes of knockout of METTL14. Finally, we observed that combined treatment with an m6A inhibitor and a MEK inhibitor synergistically suppressed JMML growth. Collectively, these findings highlight the critical role of METTL14 in JMML tumorigenesis and suggest that m6A RNA modification represents a promising therapeutic target for this disease.
Published in
The m6A methyltransferase METTL14 promotes oncogenic Kras induced juvenile myelomonocytic leukemia through dysregulating autophagy
Zhang P, Feng K, Yu X et al. · Cell death and differentiation 2026 · PMID 40819104 · doi:10.1038/s41418-025-01561-0
This dataset
Open GSE298134 in finder → Find more mouse 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 GSE298134_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 PRJNA1268412 and SRA study SRP588127. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40819104) ↗
Study design
12 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 12 sample titles: 12 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
Datasets on a similar topic

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