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Bulk RNA-sequencing results of p16INK4A-high and -low glioblastoma, IDH-wild type, grade 4.

GSE266210 Homo sapiens (human) RNA-seq (gene expression) 11 samples Submitted 2024/05/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 11 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
11 samples: 5 × p16_low vs 3 × Perilesional normal vs 3 × p16_highGroups 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
High p16(INK4A) expression in glioblastoma is associated with senescence phenotype and better prognosisNeoplasia (New York, N.Y.) · 2025
Summary (written by the submitters)
The expression of p16INK4A is widely used to identify fully senescent cells in noncancerous tissues, as well as senescent cancer cells in various types of cancer. In our study, we observed diffuse expression of p16INK4A in around 40 % of the patients with wild-type CDKN2A gene without mutation showing senescence phenotype, correlating with significant differences in overall survival between patients with high and low levels of p16INK4A expression.
Read the full summary ↓
This prognostic distinction led us to hypothesize that cellular senescence in glioblastoma cells might confer a favorable effect on patient outcomes. To investigate the mechanism underlying this phenotype, we conducted RNA sequencing on 11 samples of glioblastoma, IDH-wild type along with corresponding in vivo and in vitro studies.
Published in
High p16(INK4A) expression in glioblastoma is associated with senescence phenotype and better prognosis
Park SS, Roh TH, Tanaka Y et al. · Neoplasia (New York, N.Y.) 2025 · PMID 39724755 · doi:10.1016/j.neo.2024.101116
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 GSE266210_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 PRJNA1106382 and SRA study SRP504894. Searching any of these in the dataset finder brings you back here.

Study design
5 × p16_low vs 3 × Perilesional normal vs 3 × p16_high

Supports a between-group comparison across 11 samples.

How we read this: 3 replicated groups read from 11 sample titles; they account for 11 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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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.