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Interuption of Klf5 acetylaiton at K358 affects gene expression profiles in Pten deficient mouse prostates

GSE253523 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2024/06/24 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 20 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
Interruption of KLF5 acetylation promotes PTEN-deficient prostate cancer progression by reprogramming cancer-associated fibroblastsThe Journal of clinical investigation · 2024
Summary (written by the submitters)
Cancer associated fibroblasts (CAFs) play a pivotal role in tumor progression, but it remains elusive whether and how PTEN-deficient prostate cancers reprogram CAFs to overcome the barriers for tumor progression. Herein, we report that PTEN deficiency induces KLF5 acetylation; and interruption of KLF5 acetylation orchestrates intricate interactions between cancer cells and CAFs that enhance FGFR1 signaling and promote tumor growth.
Read the full summary ↓
Deacetylated KLF5 promotes tumor cells to secrete TNF-α, which stimulates inflammatory CAFs to release FGF9. CX3CR1 inhibition blocks FGFR1 activation triggered by FGF9 and sensitizes PTEN-deficient prostate cancer to AKT inhibitor capivasertib.
Published in
Interruption of KLF5 acetylation promotes PTEN-deficient prostate cancer progression by reprogramming cancer-associated fibroblasts
Zhang B, Liu M, Mai F et al. · The Journal of clinical investigation 2024 · PMID 38781024 · doi:10.1172/JCI175949
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 GSE253523_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 PRJNA1066205 and SRA study SRP484391. Searching any of these in the dataset finder brings you back here.

Study design
20 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 20 sample titles: 20 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
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