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Aberrant EZHIP Expression Drives Tumorigenesis in Osteosarcoma [RNA-Seq]

GSE241103 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2025/03/28 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 35 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
35 samples: 12 conditions, mostly in triplicateGroups 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
Aberrant EZHIP expression drives tumorigenesis in osteosarcomaNature communications · 2025
Summary (written by the submitters)
Osteosarcomas (OS) are aggressive bone tumors characterized by extensive structural variation, and rare recurrent oncogenic driver mutations. Here, we identify ectopic expression of the oncohistone-mimic EZHIP in more than 20% of patients, in two independent OS cohorts. Through gain- and loss-of-function experiments, we provide evidence that EZHIP expression is oncogenic, increasing the aggressive features of OS in vitro and in vivo.
Read the full summary ↓
EZHIP-mediated epigenetic rewiring promotes reactivation of developmental transcriptional programs and stalled differentiation of mesenchymal progenitors, which are skewed towards smooth muscle commitment at the expense of other lineages. Our findings of EZHIP expression as a prevalent driver in OS provide new insights into pathogenic pathways and therapeutic alternatives for this debilitating cancer.
Published in
Aberrant EZHIP expression drives tumorigenesis in osteosarcoma
Jawhar W, Danieau G, Annett A et al. · Nature communications 2025 · PMID 40695784 · doi:10.1038/s41467-025-61558-8
This dataset
Open GSE241103 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 GSE241103_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 PRJNA1006404 and SRA study SRP455682. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40695784) ↗
Study design
12 conditions, mostly in triplicate
hMSC cell line EZHIP over expression ×3 hMSC cell line WT ×3 KHOS cell line EZHIP over expression ×3 KHOS cell line WT ×3 MG63 cell line EZHIP over expression ×3 MG63 cell line WT ×3 U2OS cell line parental EZHIP ×3 U2OS cell line EZHIP knock out ×3 +4 more

Supports a between-group comparison across 35 samples.

How we read this: 12 replicated groups read from 35 sample titles; they account for 35 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
Datasets on a similar topic

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