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Coordination between the eIF2 kinase GCN2 and p53 signaling supports purine metabolism and the progression of prostate cancer

GSE260934 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/10/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
Homo sapiens (human)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
16 samples: 4 × WT Vehicle vs 4 × WT GCN2iB vs 4 × p53 KO Vehicle vs 4 × p53 KO GCN2iBGroups 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
Coordination between the eIF2 kinase GCN2 and p53 signaling supports purine metabolism and the progression of prostate cancerScience signaling · 2024
Summary (written by the submitters)
Cancers invoke various pathways to mitigate external and internal stresses to continue their growth and progression. We previously reported that the eIF2 kinase GCN2 and the integrated stress response are constitutively active in prostate cancer (PCa) and are required to maintain amino acid homeostasis needed to fuel tumor growth. However, although loss of GCN2 function reduces intracellular amino acid availability and PCa growth, there is no appreciable cell death.
Read the full summary ↓
Here, we discovered that the loss of GCN2 in PCa induces prosenescent p53 signaling. This p53 activation occurred through GCN2 inhibition-dependent reductions in purine nucleotides that impaired ribosome biogenesis and, consequently, induced the impaired ribosome biogenesis checkpoint. p53 signaling induced cell cycle arrest and senescence that promoted the survival of GCN2-deficient PCa cells. Depletion of GCN2 combined with loss of p53 or pharmacological inhibition of de novo purine biosynthesis reduced proliferation and enhanced cell death in PCa cell lines, organoids, and xenograft models. Our findings highlight the coordinated interplay between GCN2 and p53 regulation during nutrient stress and provide insight into how they could be targeted in developing new therapeutic strategies for PCa.
Published in
Coordination between the eIF2 kinase GCN2 and p53 signaling supports purine metabolism and the progression of prostate cancer
Cordova RA, Sommers NR, Law AS et al. · Science signaling 2024 · PMID 39591412 · doi:10.1126/scisignal.adp1375
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 GSE260934_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 PRJNA1084065 and SRA study SRP493483. Searching any of these in the dataset finder brings you back here.

Study design
4 × WT Vehicle vs 4 × WT GCN2iB vs 4 × p53 KO Vehicle vs 4 × p53 KO GCN2iB

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 of them. 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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