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Updated: Oct 28, 2025
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis
Fang Cao1, Yinchun Fan1, Yunhu Yu2
1Department of Cerebrovascular Disease, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, 563000, People's Republic of China.
Introduction:
As one of the most prevalent and malignant brain cancers, glioblastoma multiforme (GBM) presents a poor prognosis and the molecular mechanisms remain poorly understood. Consequently, molecular research, including various biomarkers, is essential to exploit the occurrence and development of glioma.
Methods:
Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression modules and networks based on the Chinese Glioma Genome Atlas (CGGA) glioblastoma specimens. Then, protein-protein interaction (PPI) and gene ontology (GO) analyses were performed to mine hub genes. RT-PCR and immunohistochemistry were employed to examine the expression level of GRPR, CXCL5, and CXCL11 in glioma patients.
Results:
We confirmed two gene modules by protein-protein interaction networks. Functional enrichment analysis was performed to identify the significance of gene modules. Prognostic biomarkers GRPR, CXCL5, and CXCL11 related to the survival time of GBM samples were mined in The Cancer Genome Atlas (TCGA) dataset. qRT-PCR revealed that GRPR, CXCL5, and CXCL11 led to a significant increase in GBM sample compared to control.
Conclusion:
In this study, we developed and confirmed three mRNA signatures (GRPR, CXCL5, and CXCL11) for evaluating overall survival in GBM patients. Our research assists in existing understanding of GBM diagnosis and prognosis.
