Abstract:
: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.
journal_name
Front Genetjournal_title
Frontiers in geneticsauthors
Zhang YH,Li Z,Zeng T,Pan X,Chen L,Liu D,Li H,Huang T,Cai YDdoi
10.3389/fgene.2020.604336subject
Has Abstractpub_date
2020-11-24 00:00:00pages
604336issn
1664-8021journal_volume
11pub_type
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journal_title:Frontiers in genetics
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