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Open AccessArticle

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

by Lei Chen 1,2,3,†, Tao Zeng 4,†, Xiaoyong Pan 5,6,†, Yu-Hang Zhang 7, Tao Huang 7,* and Yu-Dong Cai 1,*
School of Life Sciences, Shanghai University, Shanghai 200444, China
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
IDLab, Department for Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(17), 4269;
Received: 1 August 2019 / Revised: 19 August 2019 / Accepted: 29 August 2019 / Published: 31 August 2019
(This article belongs to the Collection Feature Papers in Molecular Biophysics)
Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes. View Full-Text
Keywords: breast cancer; subtype; methylation; pattern; multi-class classification breast cancer; subtype; methylation; pattern; multi-class classification
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Chen, L.; Zeng, T.; Pan, X.; Zhang, Y.-H.; Huang, T.; Cai, Y.-D. Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes. Int. J. Mol. Sci. 2019, 20, 4269.

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