Genetic Discrimination of Grade 3 and Grade 4 Gliomas by Artificial Neural Network

Abstract

Gliomas, including anaplastic gliomas (AG; grade 3) and glioblastomas (GBM; grade 4), are malignant brain tumors associated with poor prognosis and low survival rates. Current classification systems based on histopathology have limitations due to intratumoral heterogeneity. The treatment and prognosis are distinctly different between grade 3 and grade 4 gliomas patients. Therefore, there is a need for molecular markers to differentiate these tumors accurately. In this study, we aimed to identify a gene expression signature using an artificial neural network (ANN) in application to microarray and serial analysis of gene expression (SAGE) data for grade 3 (AG) and grade 4 (GBM) gliomas discrimination. We acquired gene expression data from publicly available datasets on glial tumors of grades 3 and 4-a total of 93 grade 3 gliomas and 224 grade 4 gliomas. To select genes for classification, we implemented an artificial neural network-based method using a combination of self-organized maps (SOM) and perceptron. In general, we implemented a multi-stage procedure that involved multiple runs of a genetic algorithm to identify genes that provided optimal clusterization on the SOM. We performed this procedure multiple times, resulting in different sets of genes each time. Eventually, we selected several genes that appeared most frequently in the reduced sets and performed classification using them. Our analysis identified a set of seven genes (BCAS4, GLUD2, KCNJ10, KCND2, AKR7A2, FOLR1, and KIAA0319). The classification accuracy using this gene set was 87.5%. These findings suggest the potential of this gene set as a molecular marker for distinguishing grade 3 (AG) from grade 4 (GBM) gliomas.

Keywords: Anaplastic glioma; Artificial neural network; Feature selection; Gene signature; Glioblastoma.