Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis

作者：Jianmin Li, Zhaozhen Zhang, Ke Guo, Shuhua Wu, Chong-Yi Guo, Xinfan Zhang, Zi Wang · 发表于：BMC Neurology · 年份：2022 · DOI：10.1186/s12883-022-02661-x · 被引用次数：5 · 研究领域：Medicine

Background Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene. Methods Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3 . OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3 . Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis. Results GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) fro...