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Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.
Pan, XiaoYong; Zeng, Tao; Yuan, Fei; Zhang, Yu-Hang; Chen, Lei; Zhu, LiuCun; Wan, SiBao; Huang, Tao; Cai, Yu-Dong.
Afiliação
  • Pan X; School of Life Sciences, Shanghai University, Shanghai, China.
  • Zeng T; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
  • Yuan F; IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium.
  • Zhang YH; Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.
  • Chen L; Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China.
  • Zhu L; Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
  • Wan S; College of Information Engineering, Shanghai Maritime University, Shanghai, China.
  • Huang T; Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, China.
  • Cai YD; School of Life Sciences, Shanghai University, Shanghai, China.
Article em En | MEDLINE | ID: mdl-31803734
ABSTRACT
Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article