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SUV39H1 epigenetically modulates the MCPIP1-AURKA signaling axis to enhance neuroblastoma tumorigenesis

GSE254118 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/12/10 Platform GPL23227
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
NeuroblastomaThis series is part of our curated briefing on it.
Samples
12 samples: 3 × SH0nM vs 3 × SH50nM vs 3 × SHsiNC vs 3 × SHsiSUVGroups 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
SUV39H1 epigenetically modulates the MCPIP1-AURKA signaling axis to enhance neuroblastoma tumorigenesisOncogene · 2024
Summary (written by the submitters)
Epigenetic regulation is a pivotal factor during neuroblastoma (NB) pathogenesis and investigations into cancer epigenetics are actively underway to identify novel therapeutic strategies for NB patients. SUV39H1, a member of the H3K9 methyltransferase family, contributing to tumorigenesis across multiple malignancies. However, its specific role in NB remains unexplored.
Read the full summary ↓
In this study, we conducted a high-throughput screen utilizing a compound library containing 288 epigenetic drugs, leading to the identification of chaetocin as the most potent NB inhibitor by targeting SUV39H1. Genetic manipulation and therapeutic inhibition of SUV39H1 significantly impacted proliferation, migration, cell cycle phases, and apoptosis in NB cells. Concurrently, chaetocin demonstrated robust anti-tumor efficacy in vivo with tolerable toxicity. RNA-seq unveiled that SUV39H1 knockdown and inhibition down-regulated cell cycle pathways, impacting vital genes such as AURKA. Besides, MCPIP1 emerged as a novel tumor suppressor following SUV39H1 inhibition, which decreased AURKA expression in NB. In detail, SUV39H1 mediated the enrichment of H3K9me3 at the promoter region of MCPIP1, repressing the MCPIP1-mediated degradation of AURKA and facilitating the subsequent accumulation of AURKA, which revealed the oncogenic role of SUV39H1 via the SUV39H1-MCPIP1-AURKA signaling axis in NB. Therapeutic inhibition of SUV39H1 using chaetocin emerges as an effective and safe strategy for NB patients.
Published in
SUV39H1 epigenetically modulates the MCPIP1-AURKA signaling axis to enhance neuroblastoma tumorigenesis
Li M, Sun F, Wang J et al. · Oncogene 2024 · PMID 39300256 · doi:10.1038/s41388-024-03164-4
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 GSE254118_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 PRJNA1068591 and SRA study SRP485790. Searching any of these in the dataset finder brings you back here.

Study design
3 × SH0nM vs 3 × SH50nM vs 3 × SHsiNC vs 3 × SHsiSUV

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 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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