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ARID1A Deficiency in Diffuse-Type Gastric Cancer Promotes a Pyrimidine Metabolic Vulnerability

GSE319750 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2026/02/20 Platform GPL28038Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Gastric cancerThis series is part of our curated briefing on it.
Samples
14 samples: 6 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
ARID1A Deficiency in Diffuse-Type Gastric Cancer Promotes a Pyrimidine Metabolic VulnerabilityMolecular cancer research : MCR · 2026
Summary (written by the submitters)
Loss-of-function mutations in ARID1A define an aggressive subtype of diffuse gastric cancer (DGC) that is often resistant to standard chemotherapy. Here, we uncover a precise metabolic vulnerability in ARID1A-deficient DGC driven by a specific transporter defect. Through integrated metabolomic and transcriptomic analyses, we demonstrate that ARID1A loss transcriptionally represses the high-affinity nucleoside transporter SLC28A3.
Read the full summary ↓
Our profiling revealed a critical lack of redundancy in the concentrative transporter family (CNT) in DGC, establishing a strict reliance on SLC28A3 for maintaining intracellular deoxycytidine (dC) pools. Consequently, ARID1A deficiency creates a severe "low-dCTP" metabolic bottleneck. We show that the dC analog Gemcitabine exploits this state through a distinct functional dichotomy: it enters via intact equilibrative transporters (ENTs) to target cells that have lost their competitive dC barrier. Mechanistically, Gemcitabine exerts a "dual-hit" effect by outcompeting the scarce dC pool for DNA incorporation while simultaneously inhibiting ribonucleotide reductase, thereby blocking de novo nucleotide synthesis. This synergistic collapse of pyrimidine metabolism was validated in patient-derived ex vivo cultures and in vivo peritoneal dissemination models. Our findings provide a robust mechanistic basis for repurposing Gemcitabine as a precision therapy for ARID1A-deficient DGC, offering a potent strategy for this intractable malignancy.
Published in
ARID1A Deficiency in Diffuse-Type Gastric Cancer Promotes a Pyrimidine Metabolic Vulnerability
Hirano H, Makinoshima H, Ogiwara H · Molecular cancer research : MCR 2026 · PMID 41757873 · doi:10.1158/1541-7786.MCR-25-1069
This dataset
Open GSE319750 in finder → Find more human RNA-seq datasets → Gastric cancer 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 GSE319750_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1425046 and SRA study SRP677800. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41757873) ↗
Study design
6 conditions, mostly in duplicate
RNA_NSC29C_NT ×2 RNA_NSC60C_NT ×2 RNA_NSC64C_NT ×2 RNA_NSC67C_NT ×2 RNA_NSC69C_NT ×2 RNA_NSC76C_NT ×2 ATAC_NSC29C_WT_siNT_72h ×1 ATAC_NSC29C_WT_siARID1A_72h ×1

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 14 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
More Gastric cancer datasets

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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.