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A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer [RNA-Seq]

GSE271053 Mus musculus (mouse) RNA-seq (gene expression) 43 samples Submitted 2026/04/13 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 43 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
43 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.
Paper
A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancerbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
Advanced prostate cancer is often associated with the emergence of more aggressive disease phenotypes, including neuroendocrine prostate cancer (NEPC), which are currently incurable. To identify drivers of aggressive prostate cancer, we conducted a Sleeping Beauty (SB) transposon mutagenesis screen based on a mouse model having conditional loss of function of Pten and Tp53 prostate (NPp53).
Read the full summary ↓
Compared with the control mice (NPp53-SB(—)), the experimental mice (NPp53-SB(+)) develop aggressive prostate cancer phenotypes that are highly heterogeneous and highly metastasis. Most notably, a high percentage of NPp53-SB(+) prostate tumors have features of NEPC, which are conserved with human prostate cancer. To identify and prioritize drivers of NEPC in these NPp53-SB(+) tumors, we used a novel integrative approach that combines (i) genomic analyses of common insert sites (CIS) for the SB transposon, (ii) VIPER analyses of control NPp53-SB(+) prostate tumors to identify master regulators (MRs) enriched in NEPC, and (iii) comparative transcriptomic analyses with data from human prostate cancer patients followed by integrative analyses using of these data using a CINDy algorithm to identify CIS-associated genes that modulate the NEPC phenotypes. Among these the nicotinamide adenosine dinucleotide (NAD)-dependent deacetylase, sirtuin 1 (Sirt1). Loss- and gain-of-function studies in human prostate cancer cell lines showed that SIRT1 promotes NEPC, while its depletion or inhibition reduces NEPC. Overall, this integrative phenotypic and systems analyses have identified of candidate drivers of NEPC and may be generalizable to analyzing and interpreting data from analogous in vivo mutagenesis screens.
Published in
A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer
de Almeida FN, Vasciaveo A, Antao AM et al. · bioRxiv : the preprint server for biology 2024 · PMID 39253480 · doi:10.1101/2024.08.24.609538
This dataset
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.

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 GSE271053_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1129506 and SRA study SRP516823. Searching any of these in the dataset finder brings you back here.

Study design
A cohort of 43 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run CMZ101 … CMZ357 and carry no group information.

Samples in this study

+ 3 more — browse all 43 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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