Distinguishing Glioblastoma Subtypes by Methylation Signatures
Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on inte...
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doaj-abdc075de6bb404382845993dcfcf5cc2020-11-25T04:12:00ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-11-011110.3389/fgene.2020.604336604336Distinguishing Glioblastoma Subtypes by Methylation SignaturesYu-Hang Zhang0Yu-Hang Zhang1Zhandong Li2Tao Zeng3Xiaoyong Pan4Lei Chen5Dejing Liu6Hao Li7Tao Huang8Yu-Dong Cai9School of Life Sciences, Shanghai University, Shanghai, ChinaChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesCollege of Food Engineering, Jilin Engineering Normal University, Changchun, ChinaShanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, ChinaInstitute 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, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai, ChinaShanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaCollege of Food Engineering, Jilin Engineering Normal University, Changchun, ChinaShanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaGlioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes’ methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.https://www.frontiersin.org/articles/10.3389/fgene.2020.604336/fullglioblastomamethylationsignaturesubtypeclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu-Hang Zhang Yu-Hang Zhang Zhandong Li Tao Zeng Xiaoyong Pan Lei Chen Dejing Liu Hao Li Tao Huang Yu-Dong Cai |
spellingShingle |
Yu-Hang Zhang Yu-Hang Zhang Zhandong Li Tao Zeng Xiaoyong Pan Lei Chen Dejing Liu Hao Li Tao Huang Yu-Dong Cai Distinguishing Glioblastoma Subtypes by Methylation Signatures Frontiers in Genetics glioblastoma methylation signature subtype classification |
author_facet |
Yu-Hang Zhang Yu-Hang Zhang Zhandong Li Tao Zeng Xiaoyong Pan Lei Chen Dejing Liu Hao Li Tao Huang Yu-Dong Cai |
author_sort |
Yu-Hang Zhang |
title |
Distinguishing Glioblastoma Subtypes by Methylation Signatures |
title_short |
Distinguishing Glioblastoma Subtypes by Methylation Signatures |
title_full |
Distinguishing Glioblastoma Subtypes by Methylation Signatures |
title_fullStr |
Distinguishing Glioblastoma Subtypes by Methylation Signatures |
title_full_unstemmed |
Distinguishing Glioblastoma Subtypes by Methylation Signatures |
title_sort |
distinguishing glioblastoma subtypes by methylation signatures |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2020-11-01 |
description |
Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes’ methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma. |
topic |
glioblastoma methylation signature subtype classification |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2020.604336/full |
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