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Distinct functions of wild-type and mutant Δ133p53α R273H differentially regulate glioblastoma aggressiveness and therapy-induced senescence

GSE240377 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2024/07/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 72 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
GlioblastomaThis series is part of our curated briefing on it.
Samples
72 samples: 72 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
Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescenceCell death & disease · 2024
Summary (written by the submitters)
Mutations effects on p53 isoforms’ activities remain largely unknown, although they are mutated in 92% of TP53 mutant cancers. Therefore, exploring the effect of mutations on p53 isoforms activities is a critical, albeit unexplored area in the p53 field. Current glioblastoma treatments, including temozolomide chemotherapy and radiations, induce cellular senescence through a p53-dependent mechanism.
Read the full summary ↓
Nevertheless, GBMs still have a poor 6.8% five-year survival indicating a need to better understand underlying mechanisms affecting the response to treatments. The progression from low-grade astrocytoma to glioblastoma is accompanied by TP53 mutations. Since Δ133p53α prevents cellular senescence, studying the impact of its mutation in glioblastoma is relevant to determine if it affects the progression and response to treatment of GBM cells. In this aim, we stably overexpressed wild-type (WT) Δ133p53α in WT glioblastoma multiforme (GBM) cells, and Δ133p53α R273H in R273H mutant GBM cells. We used mRNA-sequencing to identify mutant Δ133p53α-specific target genes. We examined proliferation, invasion, DNA repair, apoptosis, and senescence regulation by WT and mutant Δ133p53α. All results were confirmed by siRNA knock-down. Finally, using TCGA data, we investigated the association of the mutant Δ133p53α-specific target genes to clinical outcome of GBM and low-grade glioma (LGG) patients. This study provides the first and most in-depth characterization of a mutant p53 isoform’s activities to date and demonstrate that mutant Δ133p53α R273H is an active contributor to glioblastoma carcinogenesis and response to treatment. Furthermore, by discovering the link between TP53 mutation and IL4I1/IDO1/AhR pathway, we show strong evidence of Δ133p53α R273H clinical relevance in mutant TP53 glioblastoma development and aggressiveness, and as a potential therapeutic target and biomarker.
Published in
Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence
Joruiz SM, Von Muhlinen N, Horikawa I et al. · Cell death & disease 2024 · PMID 38937431 · doi:10.1038/s41419-024-06769-5
This dataset
Open GSE240377 in finder → Find more human RNA-seq datasets → Glioblastoma landscape →
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 GSE240377_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 PRJNA1003466 and SRA study SRP454052. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38937431) ↗
Study design
72 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 72 sample titles: 72 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

+ 32 more — browse all 72 samples with per-sample file links →

More Glioblastoma datasets

Search all human 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.