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Temozolomide Induces Glioblastoma Dedifferentiation into Highly Malignant Stem-like Cells Through Senescence-Associated Reprogramming via HIF1α/HIF2α Regulation [RNA-seq]

GSE282387 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/04/23 Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
12 samples: 3 × U87_sensitive_RNA-seq vs 3 × U87_resistant_RNA-seq vs 3 × U118_sensitive_RNA-seq vs 3 × U118_resistant_RNA-seqGroups 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
Temozolomide promotes glioblastoma stemness expression through senescence-associated reprogramming via HIF1α/HIF2α regulationCell death & disease · 2025
Summary (written by the submitters)
A critical issue is that recurrent glioblastoma multiforme (GBM) after temozolomide (TMZ) exposure becomes more malignant, exhibiting higher invasion and stemness than the primary tumor. However, the detailed mechanism remains to be elucidated. While the majority of GBM cells succumb to TMZ treatment, some enter cell cycle arrest, adopt a senescence-associated secretory phenotype (SASP), and activate senescence-related signaling pathways.
Read the full summary ↓
These cells later exit senescence, re-enter the cell cycle, and proliferate, forming aggregates with stemness characteristics, including high expression of stemness markers, colony formation, high invasion, migration, and chemotherapy resistance. Critically, these new aggregates promote the invasion, migration, and chemotherapy resistance of surrounding cells. Gene Set Enrichment Analysis (GSEA) and KEGG analysis of miRNA and mRNA sequences indicated that hallmark-hypoxia and HIF1-signaling pathways were activated. We verified that HIF1α and HIF2α levels changed before, during, and after TMZ treatment. Knocking out HIF1α and HIF2α in GBM cells and exposing them to TMZ resulted in fewer senescent cells and aggregates. This study clarifies how recurrent GBM becomes more malignant during and after TMZ treatment and highlights the regulatory roles of HIF1α and HIF2α, emphasizing that preventing senescence cell formation and inhibiting HIF1α and HIF2α expression are crucial for improving therapeutic outcomes.
Published in
Temozolomide promotes glioblastoma stemness expression through senescence-associated reprogramming via HIF1α/HIF2α regulation
Wang P, Liao B, Gong S et al. · Cell death & disease 2025 · PMID 40253386 · doi:10.1038/s41419-025-07617-w
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 GSE282387_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 PRJNA1188485 and SRA study SRP546377. Searching any of these in the dataset finder brings you back here.

Study design
3 × U87_sensitive_RNA-seq vs 3 × U87_resistant_RNA-seq vs 3 × U118_sensitive_RNA-seq vs 3 × U118_resistant_RNA-seq

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.