Work

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Year: 2022

Type: article

Abstract: Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

Source: Journal of Computational Biology

DOI: 10.1089/cmb.2022.0002

Authors: Ran Hu, Xianghong Jasmine Zhou, Wenyuan Li

Institution: University of California, Los Angeles

Language: English

Topic: Epigenetics and DNA Methylation

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Cancer Institute