Prediction for target genes affecting biological behaviors of glioblastoma
10.3760/cma.j.cn115354-20220711-00485
- VernacularTitle:影响胶质母细胞瘤生物学行为的靶基因预测
- Author:
Shuo WANG
1
;
Zheng CHEN
;
Yuanyuan XIE
;
Guowei TAN
Author Information
1. 厦门大学附属第一医院神经外科，厦门 361000
- Keywords:
Glioblastoma;
Differentially expressed gene;
Target gene;
Bioinformatics;
Biological behavior
- From:
Chinese Journal of Neuromedicine
2022;21(12):1203-1208
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the differentially expressed genes in glioblastoma development and their functions and roles as to identify the target genes influencing biological behaviors of glioblastoma.Methods:Original gene expression profiles of GSE70231 dataset obtained from Gene Expression Omnibus database were screened for differentially expressed genes by GEO2R software. DAVID database was used to conduct gene ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for these differentially expressed genes. STRING database was used to construct protein-protein interaction (PPI) network of these differentially expressed genes; target genes were selected from PPI network by cytoHubba and MCODE plug-ins; GEPIA online was used to analyze the expressions of target genes in glioblastoma and their influences in overall survival of glioblastoma patients (sample data collecting from The Cancer Genome Atlas database). Finally, the selected target genes were verified using RNA-seq dataset GSE50021 from human tissues.Results:Totally, 520 differentially expressed genes were identified, including 305 up-regulated genes and 215 down-regulated ones. GO analysis showed that the differentially expressed genes were mainly enriched in biological processes (signal transduction, cell adhesion, and positive regulation of cell proliferation), cytological components (extracellular exosomes, cytoplasm, and cytoplasmic membrane), and molecular function (protein binding). KEGG pathway enrichment analysis showed that the differentially expressed genes were mainly enriched in mitogen activated protein kinase signal pathway, proteoglycans in cancer, oxytocin signal pathway and calcium signal pathway. Totally, 17 target genes were selected by MCODE and cytoHubba plug-ins from the PPI network of differentially expressed genes; functional analysis and clinical sample verification showed 8 target genes ( VCAM1, SPP1, ITGB1, CTGF, VIM, ITGAV, COL1A1, and BCL2A1) could affect the biological behaviors of glioblastoma; the correlations of ITGAV, COL1A1, and BCL2A1 with glioma had been rarely reported, and GSE50021 dataset verified that their expressions in glioblastoma tissues were significantly higher than those in normal brain tissues ( P<0.05). Conclusion:These 8 target genes concluded from this research, especially BCL2A1, COL1A1 and ITGAV, may be important targets for exploring the pathogenesis, diagnosis and treatment of glioblastoma in the future.
