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TOP2A inhibition and its cellular effects related to cell cycle checkpoint adaptation pathway

GSE286076 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/02/01 Platform GPL24676
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 × WT_NT vs 3 × WT_ICRF193 vs 3 × MCPH1_NT vs 3 × MCPH1_ICRF193Groups 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
TOP2A inhibition and its cellular effects related to cell cycle checkpoint adaptation pathwayScientific reports · 2025
Summary (written by the submitters)
In this study, we investigate the G2 checkpoint activated by chromosome entanglements, the so-called Decatenation Checkpoint (DC), which can be activated by TOP2A catalytic inhibition. Specifically, we focus on the spontaneous ability of cells to bypass or override this checkpoint, referred to as checkpoint adaptation. Some factors involved in adapting to this checkpoint are p53 and MCPH1.
Read the full summary ↓
Using cellular models depleted of p53 or both p53 and MCPH1 in hTERT-RPE1 cells, we analyzed cell cycle dynamics and adaptation, segregation defects, apoptosis rate, and transcriptional changes related to prolonged exposure to TOP2A inhibitors. Our findings reveal that cell cycle dynamics are altered in MCPH1-depleted cells compared to control cells. We found that MCPH1 depletion can restore the robustness of the DC in a p53-negative background. Furthermore, this research highlights the differential effects of TOP2A poisons and catalytic inhibitors on cellular outcomes and transcriptional profiles. By examining the different mechanisms of TOP2A inhibition and their impact on cellular processes, this study contributes to a deeper understanding of the regulation and physiological implications of the DC and checkpoint adaptation in non-carcinogenic cell lines.
Published in
TOP2A inhibition and its cellular effects related to cell cycle checkpoint adaptation pathway
Arroyo M, Fernández-Mimbrera MA, Gollini E et al. · Scientific reports 2025 · PMID 39885205 · doi:10.1038/s41598-025-87895-8
This dataset
Open GSE286076 in finder → Find more human RNA-seq datasets →
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 GSE286076_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 PRJNA1205784 and SRA study SRP555114. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39885205) ↗
Study design
3 × WT_NT vs 3 × WT_ICRF193 vs 3 × MCPH1_NT vs 3 × MCPH1_ICRF193

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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