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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   (573 HTML777 PDF(pc) (3564KB)(929)  

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.


Databases Website
STRING https://string-db.org/
GEPIA http://gepia.cancer-pku.cn/
CGGA http://www.cbioportal.org
Tab. 2 Databases related to bioinformatics analysis
Extracts from the Article
基因功能及通路分析:利用R工具“org.Hs.eg.db”“clusterProfiler”“enrichplot”对DEG进行基因本体(Gene Ontology,GO)和京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)信号富集分析。GO分析包括生物过程(biological process)、细胞组成(cellular component)和分子功能(molecular function)3个部分。利用STRING工具构建蛋白质相互作用(protein-protein interaction,PPI)网络,寻找蛋白质之间的相互作用关系。Hub基因的筛选通过PPI网络,利用Cytoscape中的“Hub”计算生成。生存分析、表达分析使用GEPIA和CGGA数据库相关在线Survival分析工具。STRING、GEPIA和CGGA数据库网址见表2。
胶质母细胞瘤具有高度异质性,其进化过程表现出高度分支化特征,按照分支模式及进化速率的估算,与复发相关的克隆往往在诊断前已存在。接受替莫唑胺治疗的胶质母细胞瘤患者中有17%(17/100)复发并伴有高突变肿瘤,但未接受替莫唑胺治疗的患者中没有发生高突变[13]。Nandeesh等[14]研究表明,复发性肿瘤中SOX2表达的显著增加可能表明这些肿瘤中存在具有干样特性的未分化细胞,这种改变可能增加的侵袭性和干性,抵抗放疗和化疗。Zhang等[15]将23例患者切除的标本进行了外显子组测序,该组病例均为初始低级别胶质瘤,而后出现复发性肿瘤,在43%的病例中,至少有一半的初始肿瘤突变在复发时未被发现,包括TP53、ATRXSMARCA4和BRAF中的驱动突变,10例接受替莫唑胺治疗的患者中有6例肿瘤发展为高级别胶质瘤,复发肿瘤发生超突变,并带有TMZ诱导突变特征,即视网膜母细胞瘤(retinoblastoma,RB)和蛋白激酶B(protein kinase B,AKT)-哺乳动物雷帕霉素靶蛋白(mammalian targets of rapamycin,mTOR)的驱动突变[15]。Kim等[16]通过采用多基因组学方法分析初次和再次手术的26对配对脑胶质瘤标本,证实复发胶质瘤获得了大量新的基因突变。胶质母细胞瘤复发的两种肿瘤进化途径:大多数局部复发的肿瘤共有大多数初始肿瘤突变,与线性进化一致。在远处复发时,初始和复发肿瘤之间的遗传差异导致关键胶质母细胞瘤驱动基因/途径改变的变化。本研究所选用两个数据集分析产生的DEG交集并不多,这可能是因为胶质母细胞瘤复发后驱动复发增长的基因改变较多,与初始肿瘤不同。
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