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Screening recurrent glioblastoma-related genes and analyzing their gene expressions in association with clinicopathological parameters and prognosis
LIN Yi, WANG Ce, KANG Xun, KANG Zhuang, CHEN Feng, JIANG Bo, LI Wenbin
China Oncology    2022, 32 (1): 13-23.   DOI: 10.19401/j.cnki.1007-3639.2022.01.002
Abstract   (572 HTML777 PDF(pc) (3564KB)(928)  

Background and purpose: Glioma is the most common and malignant primary brain tumor in the central nervous system (CNS). Glioblastoma is highly malignant and aggressive, and the prognosis of patients with recurrent glioblastoma is very poor. This study aimed to screen the genes related to the recurrent glioblastoma, and analyze the relationship between their expressions, clinicopathological parameters and prognosis in glioma. Methods: By mining the relevant datasets of the primary and recurrent cases of glioblastoma in the GEO database, the differentially expressed gene (DEG) in the samples of primary and recurrent glioblastomas were screened and analyzed. All DEGs analyses were carried out in ontology function and pathway enrichment. Protein-protein interaction (PPI) network was constructed and used for screening Hub gene. Key genes were intersected by PPI network and Venn diagram, and the Gene Expression Profiling Interactive Analysis (GEPIA) and Chinese Glioma Genome Atlas (CGGA) database were analyzed for association of key gene expressions and survival status. Key genes were furtherly analyzed to determine the relationship between their expressions and clinicopathological parameters of glioma. Results: There were 40 DEG screened in the dataset GSE62153, including 34 up-regulated genes and 6 down-regulated genes. There were 19 DEG screened in the dataset GSE58399, including 16 up-regulated genes and 3 down-regulated genes. Go functional analyses showed that the DEG of GSE62153 were mainly involved in 11 physiological processes, such as central nervous system development, myelin sheath, actin binding, central nervous system myelination. The DEG of GSE58399 were mainly enriched in the positive regulation of epithelial cell migration. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment showed that the datasets GSE62153 and GSE58399 were both enriched in histidine metabolism. By using the STRING database, the core of PPI network was constructed with 20 protein molecules. A total of 10 hub genes were screened, including MOBP, OPALIN, ERMN, PLP1, MOG, CLDN11, ASPA, TMEM125, KLK6 and NKX6-2 gene. The key genes for recurrent glioblastoma were ERMN, MOG and MOBP gene. Based on analyses using The Cancer Genome Atlas (TCGA) and CGGA databases, the prognosis of patients with high expressions of ERMN, MOG and MOBP was favorable compared with the low expression group. The expression levels of key genes in glioblastoma were lower compared with the control tissues (P<0.001). There were significant differences in the expressions of ERMN, MOG and MOBP gene among different World Health Organization (WHO) grades (WHO Ⅱ, Ⅲ and Ⅳ) (P<0.001). As the grade of glioblastoma increased, the expressions of ERMN, MOG and MOBP were decreased gradually. The expressions of ERMN, MOG and MOBP gene were correlated with WHO classification, isocitrate dehydrogenase (IDH) status and clinicopathological characteristics (P<0.001). The expression of MOBP gene was correlated with age (P<0.001) and MGMT methylation status (P=0.022). Conclusion: ERMN, MOG and MOBP gene may function as tumor suppressor genes and participate in the recurrence of glioblastoma. The histidine metabolism pathway may be related to the sensitivity of methotrexate treatment.


Datasets Case n Primary GBM n Recurrent GBM n Platforms
GSE62153 43 25 18 GPL10558, illumina human HT-12 V4.0 expression beadchip
GSE58399 105 72 33 GPL6244,affymetrix human gene 1.0 ST Array
Tab. 1 Information about the GEO data and series
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基因芯片数据来源于GEO( https://www.ncbi.nlm.nih.gov/geo/)数据库,检索策略为Recurrent[All Fields] AND (Glioblastoma[MeSH Terms] OR Glioblastoma[All Fields])。研究纳入标准为采用手术切除样本(原发肿瘤和复发肿瘤均大于10例)进行RNA检测的数据集,排除标准为采用细胞培养或动物模型(Xenograft)等处理过的样本研究。数据集GSE62153和GSE58399符合研究标准,包含胶质母细胞瘤和复发胶质母细胞瘤患者组织标本的样本,表达数据和实验平台等信息见表1。研究通过R软件,下载了标准化的表达数据,利用Biobase、GEOquery和limma等数据包,寻找胶质母细胞瘤复发相关的差异表达基因(differentially expressed gene,DEG),并以P<0.05,logFC>1或logFC<-1作为筛选条件进行后续分析。
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