Related Experiment Video
Updated: Feb 25, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Identification of potential key genes associated with glioblastoma based on the gene expression profile
Lijuan Bo1, Bo Wei2, Chaohui Li2
1Department of Infections, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China.
Abstract:
Gliomas are serious primary brain tumors. The aim of the present study was to identify potential key genes associated with the progression of gliomas. The GSE31262 gene expression profile data, which included 9 glioblastoma stem cells (GSCs) samples and 5 neural stem cell samples from adult humans, were downloaded from Gene Expression Omnibus (GEO) database. limma package was used to identify differentially expressed genes (DEGs). Based on STRING database and Pearson Correlation Coefficient (PCC), a co-expression network was constructed to comprehensively understand the interactions between DEGs, and function analysis of genes in the network was conducted. Furthermore, the DEGs that were associated with prognosis were analyzed. A total of 431 DEGs were identified, including 98 upregulated DEGs and 333 downregulated DEGs. Genes including PDZ binding kinase, topoisomerase (DNA) II α (TOP2A), cyclin dependent kinase (CDK) 1, cell division cycle 6 and NIMA related kinase 2 had a relatively high degree in the co-expression network. A set of genes including cyclin D1, CDK1 and CDK2 were significantly enriched in the cell cycle and p53 signaling pathway. Additionally, 69 DEGs were identified as genes involved in glioblastoma prognosis, such as CDK2 and TOP2A. The genes that had a higher degree and were associated with cell cycle and p53 signaling pathway may play pivotal roles in the progress of glioblastoma.

