In:
Frontiers in Genetics, Frontiers Media SA, Vol. 11 ( 2020-11-24)
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.
Type of Medium:
Online Resource
ISSN:
1664-8021
DOI:
10.3389/fgene.2020.604336
DOI:
10.3389/fgene.2020.604336.s001
DOI:
10.3389/fgene.2020.604336.s002
DOI:
10.3389/fgene.2020.604336.s003
DOI:
10.3389/fgene.2020.604336.s004
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2020
detail.hit.zdb_id:
2606823-0
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