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METTL3-Driven RNA Modifications: A Key Mechanism and Potential Therapeutic Target in Pancreatic Acinar Cell Carcinoma in Mice [MeRIP]

GSE305529 Mus musculus (mouse) Single-cell RNA-seq 48 samples Submitted 2026/04/12 Platform GPL17021
At a glance

Single-cell RNA-seq · mouse · 48 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
48 samples: 48 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.
Summary (written by the submitters)
Pancreatic acinar cell carcinoma (ACC) represents a rare and aggressive malignancy with poorly understood molecular mechanisms. N6-methyladenosine (m6A) RNA modification, particularly through the methyltransferase METTL3, has emerged as a critical regulator in various cancers. This study investigates the role of METTL3-mediated RNA methylation in ACC development and progression.
Read the full summary ↓
We utilized transgenic mouse models over-expressing Mettl3 and SV40 large T antigen under the pancreatic Elastase I promoter. Comprehensive analyses included m6A-methylated RNA immunoprecipitation sequencing (MeRIP-seq), single-cell RNA sequencing (scRNA-seq), and functional studies with the METTL3 inhibitor STM2457. SAM-binding domain deletion mutants were generated to assess functional requirements. Mettl3 overexpression significantly accelerated ACC development and enhanced tumor aggressiveness. The SAM-binding domain proved essential for tumor formation, as deletion mutants failed to promote carcinogenesis. MeRIP-seq revealed preferential methylation of cell cycle and DNA replication genes in Mettl3-overexpressing tumors. scRNA-seq analysis demonstrated enhanced malignancy signatures, including epithelial-to-mesenchymal transition and TGF-β signaling. METTL3 promoted PRSS1-mediated signaling from ACC cells to inflammatory cancer-associated fibroblasts, creating a feed-forward loop involving IGF1 that amplifies tumor growth. Conditional Mettl3 deletion induced rapid tumor apoptosis. Pharmacological inhibition with STM2457 similarly triggered caspase-3/7-dependent apoptosis in pancreatic tumors. METTL3-mediated RNA methylation drives ACC pathogenesis through tumor-intrinsic cell cycle regulation and tumor-extrinsic stromal interactions. These findings establish METTL3 as a promising therapeutic target and provide mechanistic insights supporting clinical development of METTL3 inhibitors for pancreatic cancer treatment.
This dataset
Open GSE305529 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 GSE305529_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1306392 and SRA study SRP609110. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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