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PARP inhibitor augments anti-tumor efficacy of DNMT inhibitor in cholangiocarcinoma

GSE275134 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/06/18 Platform GPL16791
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)
Topic
CholangiocarcinomaThis series is part of our curated briefing on it.
Samples
12 samples: 12 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
PARP inhibitor augments anti-tumor efficacy of DNMT inhibitor by inducing senescence in cholangiocarcinomaInternational journal of biological sciences · 2025
Summary (written by the submitters)
Cholangiocarcinoma (CCA) is a dismal, heterogeneous malignancy with limited treatment options. One of the key epigenetic dysregulations in CCA is aberrant DNA methylation, suggesting that targeted DNA methylation is a promising therapeutic strategy for this disease. However, the clinical utility of DNA demethylation reagents is not well documented.
Read the full summary ↓
Here, we established patient-derived CCA cell lines and demonstrated that the demethylating agents decitabine and 5-azacytidine had minimal effects on antitumor proliferation of CCA cells. A combinatorial drug screen using a kinase inhibitor library identified PARP inhibitors as sensitizers that synergistically enhanced the antitumor effects of decitabine. The combination of DNMT inhibitors and PARP inhibitors significantly blocks tumor growth in both CCA patient-derived xenograft models and the somatic CCA model induced by hydrodynamic tail vein injection. Transcriptomic profiling analysis showed that the combination treatment activated the inflammatory signaling pathway and upregulated senescence-associated secretory phenotype gene expression, resulting in genomic instability in CCA. Mechanistically, combination treatment disrupted XRCC1 function and increased PARP1 trapping, leading to persistent DNA damage and senescence. Together, our study provides a preclinical proof-of-concept for the use of DNMT inhibitors in combination with PARP inhibitors as a novel therapeutic strategy and potentially optimizes current clinical practice in the treatment of CCA.
Published in
PARP inhibitor augments anti-tumor efficacy of DNMT inhibitor by inducing senescence in cholangiocarcinoma
Wang P, Xiao R, Chen J et al. · International journal of biological sciences 2025 · PMID 40520005 · doi:10.7150/ijbs.110947
This dataset
Open GSE275134 in finder → Find more human RNA-seq datasets → Cholangiocarcinoma 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 GSE275134_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 PRJNA1149664 and SRA study SRP527277. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40520005) ↗
Study design
12 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 12 sample titles: 12 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
More Cholangiocarcinoma datasets

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