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.
Highlights
Glioblastoma, called as glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain
We generated a series of feature subsets from the Monte Carlo feature selection (MCFS) feature list and subjected them to incremental feature selection (IFS) with support vector machine (SVM), random forest (RF), and repeated incremental pruning to produce error reduction (RIPPER) to obtain the best features for classifying different categories of GBM samples
An IFS curve was plotted with number of used features as X-axis and Matthew Correlation Coefficients (MCC) as the Y-axis for each classification algorithm, as shown in Figure 2, in which the highest MCC of each classification is marked
Summary
Glioblastoma, called as glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. The cause of this disease is unclear. The risk factors of GBM include genetic factors and environmental factors, such as smoking and exposure to pesticides. Similar to other brain cancers, GBM can cause epilepsy, nausea, vomiting, headaches, and mild hemiplegia. The typical symptoms of glioblastoma are deteriorating memory and personality or decline in Distinguishing Glioblastoma Subtypes neurological function. Most symptoms are caused by the destruction of the temporal lobes and the frontal lobes. Different subspecies of glioblastomas are produced in the central nervous system, and cancer cells in GBM are similar to stem cells
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