Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (31701151)
  • Natural Science Foundation of Shanghai Municipality (17ZR1412500)
  • National Key R&D Program of China (2018YFC0910403)
  • Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)
  • Shanghai Sailing Program (16YF1413800)
  • Youth Innovation Promotion Association of the Chinese Academy of Sciences (2016245)
  • Fund of the Key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703)
  • Science and Technology Commission of Shanghai Municipality (18dz2271000)

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