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p53 deficient breast cancer cells reprogram pre-adipocytes towards tumor protective immunomodulatory cells (WEP)

GSE238231 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/07/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
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.
Summary (written by the submitters)
The TP53 gene is mutated in approximately 30% of all breast cancer cases. Adipocytes and preadipocytes, which constitute a substantial fraction of the stroma of normal mammary tissue and of breast tumors, undergo transcriptional, metabolic and phenotypic reprogramming during breast cancer development and play an important role in tumor progression.
Read the full summary ↓
We report here that p53 loss in breast cancer cells facilitates the reprogramming of preadipocytes, inducing them to acquire a unique transcriptional and metabolic program that combines impaired adipocytic differentiation with augmented cytokine expression. This, in turn, promotes the establishment of an immunosuppressive tumor microenvironment, including increased abundance of myeloid-derived suppressor cells and elevated expression of the checkpoint ligand PD-L1. We also describe a potential gain-of-function effect of several common p53 missense mutations on the inflammatory reprogramming of preadipocytes. Altogether, our study implicates p53 loss in breast cancer cells as a driver of tumor-supportive adipose tissue reprogramming, expanding the network of non-cell autonomous mechanisms whereby p53 may act as a tumor suppressor. Further elucidation of the interplay between p53 mutations and adipocytes within the tumor microenvironment may suggest novel therapeutic targets for the treatment of breast cancer patients.
This dataset
Open GSE238231 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 GSE238231_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 PRJNA998402 and SRA study SRP451347. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.