GEO series

Drosha loss drives pineoblastoma by upregulating Ccnd2 [CUT&RUN]

GSE255908 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2024/10/03 Platform GPL30172

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 10 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Mus musculus (mouse)
Samples: 10 samples
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)

Mutations in the microRNA processing genes DICER1 and DROSHA drive childhood cancers such as pineoblastoma, a brain tumor that resembles undifferentiated precursors of the pineal gland. To understand how microRNAs regulate proliferation of these progenitors, we developed a mouse model of pineoblastoma by ablating Drosha or Dicer1 in the developing pineal gland.

Read the full summary ↓

The tumors that arise in these models resemble proliferative, undifferentiated embryonic progenitors. They are marked by loss of microRNAs, including the let-7 family, and de-repression of let-7 target genes. One upregulated let-7 target gene is the oncofetal transcription factor Plagl2, which activates expression of key pro-growth genes Igf2 and Ccnd2, and we show that small molecules targeting these two drivers block tumor growth. Thus, our results demonstrate that tumors driven by loss of microRNA processing may be therapeutically targeted by inhibiting downstream drivers of proliferation.

This dataset

Open GSE255908 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE255908_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1076932 and SRA study SRP489931. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 10 more — browse all 10 samples with per-sample file links →

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