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Immunomodulatory properties of slowly cycling and mesenchymal-like castration-resistant prostate cancer stem cells

GSE123160 Mus musculus (mouse) RNA-seq (gene expression) 70 samples Submitted 2025/01/31 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 70 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
70 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)
Tumors are constituted by different subpopulations of cells; this heterogeneity has multiple dimensions -genetic, epigenetic, and level of differentiation- with several clinical implications. On this work we outline the hierarchical organization of the normal epithelial of the prostate gland, and we observe that this organization is co-opted by tumors arising from this tissue.
Read the full summary ↓
We uncovered two subsets of normal and cancer stem cells in different mouse models of prostate cancer; one population is largely quiescent with an EMT phenotype, and the other more committed toward epithelial differentiation and poised for activation in response to androgen stimuli. Whole exome sequencing revealed common pathways to these cells; exposing the prominence of integrin signaling and focal adhesion kinase (FAK) on the stem/progenitor cells behavior. Also we start to unveil a previously unnoticed property of cancer stem cells, the ability to modulate the tumor immune microenvironment and we link this function to downstream FAK signaling.
This dataset
Open GSE123160 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 GSE123160_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 PRJNA507726 and SRA study SRP171583. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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