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DNA demethylating agents represent a therapeutic opportunity for synovial sarcoma [RNA-Seq 2]

GSE291312 Homo sapiens (human) RNA-seq (gene expression) 23 samples Submitted 2025/04/18 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · human · 23 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
Soft tissue sarcomaThis series is part of our curated briefing on it.
Samples
23 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
DNA demethylating agents suppress preclinical models of synovial sarcomaThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Synovial sarcoma is an aggressive soft tissue cancer driven by the chimeric SS18::SSX fusion oncoprotein, which disrupts chromatin remodeling by combining two antagonistic transcriptional regulators. SS18 participates in BAF complexes that open chromatin, while the SSX genes are cancer-testis antigens that interface with chromatin decorated with monoubiquitinated histone H2A placed by Polycomb repressive complexes (PRCs) activity.
Read the full summary ↓
Because KDM2B brings PRC to unmethylated CpG islands, it is plausible that methylation directly determines the distribution of SS18::SSX to target loci. Given that synovial sarcoma is also characterized by a peculiarly low DNA hypomethylation profile, we hypothesized that further disturbance of DNA methylation would have a negative impact on synovial sarcoma growth. DNMT1 disruption by CRISPR/Cas9 targeting or pharmacologic inhibition with cytidine analogs 5-aza-2ʹ-deoxycytidine (decitabine) and 5-azacytidine led to decreased genome-wide methylation, redistribution of SS18::SSX, and altered gene expression profiles, most prominently including upregulation of tumor suppressor genes, immune-related genes, and mesenchymal differentiation-related genes. These drugs suppressed growth of synovial sarcoma cell lines and drove cytoreduction in mouse genetic models. DNMT1 inhibitors, already approved for treating myelodysplastic syndromes, warrant further clinical investigation for synovial sarcoma as repurposed, targeted treatments exploiting a vulnerability in the intrinsic biology of this cancer.
Published in
DNA demethylating agents suppress preclinical models of synovial sarcoma
Hasegawa N, Benabdallah NS, Smith-Fry K et al. · The Journal of clinical investigation 2025 · PMID 40299558 · doi:10.1172/JCI190855
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 GSE291312_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 PRJNA1232808 and SRA study SRP568513. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
HSSY5A ×2 HSSYD ×2 HSSYC ×2 SYO5A ×2 SYOD ×2 SYOC ×2 2467 ×1 2800 ×1 +9 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 23 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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