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DICER1 dysregulation triggers miRNA reprogramming, promotes mesenchymal cell fates and slows TNBC tumorigenic phenotypes

GSE299101 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/02/18 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)
Topic
Breast cancerThis series is part of our curated briefing on it.
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
DICER1 dysregulation triggers reprogramming of a specific miRNA subset, promotes mesenchymal cell fates and slows TNBC tumorigenic phenotypesNon-coding RNA research · 2026
Summary (written by the submitters)
DICER1 is a key processing enzyme required for the generation of mature and active miRNAs. Mutations that diminish DICER1 function result in widespread changes in miRNA levels, resulting in changes in the transcriptome and cellular phenotypes. Previously, we have found that mutations within the 3’UTR of the Dicer1 mRNA greatly diminish DICER1 protein levels and miRNA production.
Read the full summary ↓
Triple negative breast cancer cells that contain these mutations have slower growth and migration and diminished tumorigenic potential. By comparing the transcriptome and miRNA profile of these cells, we find that miR30d-5p are significantly reduced in DICER1 mutant cells. This reduction in miR30d-5p results in increased mRNA stability and protein levels of the miR30d-5p target, SNAIL, a transcription factor that promotes the transcription of genes required for mesenchymal cell fates. We show that elevated SNAIL protein levels drive an epithelial- mesenchymal transition in these cells that can be partially rescued by the addition of miR30d-5p.Our results highlight the important role for DICER1 and miRNA levels in modulating cell fate in breast cancer.
Published in
DICER1 dysregulation triggers reprogramming of a specific miRNA subset, promotes mesenchymal cell fates and slows TNBC tumorigenic phenotypes
Koshre G, Khan M, Rajasekaran S et al. · Non-coding RNA research 2026 · PMID 41675577 · doi:10.1016/j.ncrna.2026.01.011
This dataset
Open GSE299101 in finder → Find more human RNA-seq datasets → Breast cancer 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 GSE299101_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 PRJNA1272585 and SRA study SRP590334. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41675577) ↗
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
More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

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