UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

Javaid, H; Barberis, A; Chervova, O; Nassiri, I; Voloshin, V; Sato, Y; Ogawa, S; ... Humphrey, TC; + view all (2023) A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation. BMC Cancer , 23 (1) , Article 721. 10.1186/s12885-023-11162-0. Green open access

[thumbnail of s12885-023-11162-0.pdf]
Preview
PDF
s12885-023-11162-0.pdf - Published Version

Download (3MB) | Preview

Abstract

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

Type: Article
Title: A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s12885-023-11162-0
Publisher version: https://doi.org/10.1186/s12885-023-11162-0
Language: English
Additional information: © 2023 BioMed Central Ltd. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: DNA methylation, SETD2, H3K36me3, Renal cancer biomarker, Machine learning biomarker
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Epidemiology and Public Health
URI: https://discovery.ucl.ac.uk/id/eprint/10175515
Downloads since deposit
11Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item