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Transcriptomic footprinting following restoration of p53 sequence and function utilising Adenine Base Editing

GSE287868 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2025/06/16 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 25 conditions, mostly in duplicateGroups 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
A base editing platform for the correction of cancer driver mutations unmasks conserved p53 transcription programsGenome biology · 2025
Summary (written by the submitters)
The tumor suppressor TP53 is the most frequently mutated gene in human cancer. Here, we utilised a Adenine Base Editing system to efficiently repair the p53-R273H hotspot mutation in four cancer cell lines of different origins followed by transcriptomic analysis at three different timepoints to unveil the molecular network of p53 tumor suppression.
Read the full summary ↓
Moreover, we were able to repair the p53-R175H hotspot in two cell lines of different origins. Our data demonstrates that recovery of wildtype TP53 leads to restoration of p53 function and suggests a preserved network of p53's tumor-suppressive activities following mutation repair that is irrespective of cell type, mutation profile, and epigenetic cell state. Furthermore, we attempted correcting a nonsense mutation in the tumor suppressor gene SMAD4 in one cell line leading to a progressive depletion of cancer cells, albeit with a milder phenotype compared to the one following TP53 repair. Taken together, our data provide a molecular framework following driver mutations repair, besides providing insights into key players orchestrating transcription leading to cell apoptosis. Our findings demonstrate the utility of this Base Editing system to systematically interrogate the functional consequences of cancer driver mutations and the downstream effects on gene expression.
Published in
A base editing platform for the correction of cancer driver mutations unmasks conserved p53 transcription programs
Wang P, Sen R, Buchholz F et al. · Genome biology 2025 · PMID 40696461 · doi:10.1186/s13059-025-03667-7
This dataset
Open GSE287868 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 GSE287868_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 PRJNA1214834 and SRA study SRP559478. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40696461) ↗
Study design
25 conditions, mostly in duplicate
PANC1_ABE_sgR273H_36hpi ×3 PANC1_ABE_sgR273H_48hpi ×3 PANC1_ABE_sgR273H_72hpi ×3 PANC1_WT ×3 A431_ABE_sgR273H_36hpi ×2 A431_ABE_sgR273H_48hpi ×2 A431_ABE_sgR273H_72hpi ×2 A431_ABE_sgVEGF_36hpi ×2 +17 more

Supports a between-group comparison across 54 samples.

How we read this: 25 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

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