Weighted Gene Co-expression Network Analysis of Glioblastoma Gene Expression Microarray Data

Abstract

Glioblastoma is a highly aggressive and lethal form of brain cancer characterized by its complex molecular landscape. Understanding the underlying gene expression patterns and their relationships is essential for unraveling the mechanisms driving this disease. In this study, we conducted a Weighted Gene Co-expression Network Analysis (WGCNA) on Glioblastoma gene expression microarray data to identify co-expressed gene modules and potential key regulatory genes associated with the disease. Utilizing a comprehensive dataset of Glioblastoma samples, we performed quality control and preprocessing to ensure the reliability of the data. WGCNA was employed to construct a weighted gene co-expression network, enabling the identification of modules of co-expressed genes. The correlation between these modules and clinical characteristics such as patient survival, tumor grade, and other relevant factors was assessed. Additionally, we conducted functional enrichment analysis to gain insights into the biological processes and pathways associated with the identified gene modules. Our findings revealed distinct gene modules associated with Glioblastoma progression and patient outcomes. Notably, we identified key hub genes within these modules, which may serve as potential biomarkers or therapeutic targets. Furthermore, functional enrichment analysis provided a comprehensive understanding of the biological processes and pathways influenced by these co-expressed gene modules. In conclusion, our Weighted Gene Co-expression Network analysis of Glioblastoma gene expression microarray data has shed light on the complex gene interactions and regulatory networks underlying this aggressive brain cancer. This knowledge may ultimately contribute to the development of novel diagnostic and therapeutic strategies, improving the prognosis for Glioblastoma patients.