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

Journal of computational biology: A journal of computational molecular cell biology

article

Xianghong Jasmine Zhou¹  Ran Hu¹  Wenyuan Li¹ 

[1] Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles;Institute for Quantitative & Computational Biosciences, University of California at Los Angeles;Bioinformatics Interdepartmental Graduate Program, University of California at Los Angeles

关键词: cancer prognosis;    DNA methylation;    feature selection;    high dimensionality;    prognostic model;   

DOI  :  10.1089/cmb.2022.0002

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

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.

【 授权许可】

Unknown