A Bayesian model for identifying cancer subtypes from paired methylation profiles

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of pr...

Full description

Saved in:
Bibliographic Details
Published in:Briefings in bioinformatics 2023-01, Vol.24 (1)
Main Authors: Fan, Yetian, S Chan, April, Zhu, Jun, Yi Leung, Suet, Fan, Xiaodan
Format: Article
Language:English
Subjects:
Bayes Theorem
DNA Methylation
Humans
Neoplasms - genetics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbac568