Identification of biomarkers for the transition from low-grade glioma to secondary glioblastoma by an integrated bioinformatic analysis.

Secondary glioblastoma (sGBM) is a type of glioblastoma multiforme that evolves from low-grade glioma (LGG). However, the mechanism of this transition still remains poorly understood. In this study, we used weighted gene co-expression network analysis (WGCNA) on the gene expression profiles of glioma samples from the Chinese Glioma Genome Atlas (CGGA) database to identify key genetic module related to distinguish histological characteristics. Here, the brown module was highly correlated with histological characteristics and was selected as the hub module. By applying functional annotation analysis, we found that biological processes related to the cell-cycle and DNA-replication were enriched in the genes of the brown module. After constructing a protein-protein interaction (PPI) network, validation of differential gene expression, and survival analyses, we ultimately identified five hub genes: CCNB2 (Cyclin B2), KIF2C (Kinesin Family Member 2C), CDC20 (Cell Division Cycle 20), TPX2 (TPX2 Microtubule Nucleation Factor), and PLK1 (Polo Like Kinase 1). In addition, a computational risk model was developed for predicting the clinical outcomes of sGBM patients by combining gene expression levels. This gene signature was demonstrated to be an independent predictor of survival by univariate and multivariable Cox regression analysis. Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the responses of sGBM patients to routine chemotherapeutic drugs. Patients from the high-risk group were more sensitive to common chemotherapies during clinical treatment. Our findings based on comprehensive analyses might advance the understanding of sGBM transition and aid the development of novel biomarkers for diagnosing and predicting the survival of sGBM patients.