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石磊, 王建祥, 曹成安, 彭翔. 基因芯片筛选多形性胶质母细胞瘤差异表达基因和通路[J]. 肿瘤防治研究, 2018, 45(7): 441-446. DOI: 10.3971/j.issn.1000-8578.2018.17.1403
引用本文: 石磊, 王建祥, 曹成安, 彭翔. 基因芯片筛选多形性胶质母细胞瘤差异表达基因和通路[J]. 肿瘤防治研究, 2018, 45(7): 441-446. DOI: 10.3971/j.issn.1000-8578.2018.17.1403
SHI Lei, WANG Jianxiang, CAO Cheng'an, PENG Xiang. Identification of Differently Expressed Genes and Pathways in Glioblastoma Multiforme Using Microarray[J]. Cancer Research on Prevention and Treatment, 2018, 45(7): 441-446. DOI: 10.3971/j.issn.1000-8578.2018.17.1403
Citation: SHI Lei, WANG Jianxiang, CAO Cheng'an, PENG Xiang. Identification of Differently Expressed Genes and Pathways in Glioblastoma Multiforme Using Microarray[J]. Cancer Research on Prevention and Treatment, 2018, 45(7): 441-446. DOI: 10.3971/j.issn.1000-8578.2018.17.1403

基因芯片筛选多形性胶质母细胞瘤差异表达基因和通路

Identification of Differently Expressed Genes and Pathways in Glioblastoma Multiforme Using Microarray

  • 摘要:
    目的 利用基因芯片技术和生物信息学分析方法,筛选出多形性胶质母细胞瘤相关的核心基因和信号通路,为寻找多形性胶质母细胞瘤早期诊断和靶向治疗潜在标志物提供依据。
    方法 从GEO数据库中获取多形性胶质母细胞瘤mRNA表达谱芯片原始数据,利用R软件分析得到明显差异表达基因(differentially expressed genes, DEGs),对DEGs进行功能注释(GO ontology)和KEGG信号通路(KEGG signaling pathway)富集,进一步构建蛋白质相互作用网络(protein-protein interaction network, PPI),筛选核心基因,最后利用TCGA肿瘤数据库进行验证。
    结果 通过Pearson聚类分析发现肿瘤和正常组织聚类区分明显,说明表达谱结果可靠;差异基因共2 142个,其中上调基因968个,下调基因1 174个;GO和KEGG富集结果显示,差异基因的功能主要涉及细胞周期、细胞分裂和增殖、突触传递等生物学功能和通路,通路网络分析表明MAPK信号通路起核心调控地位。通过构建PPI网络筛选出9个与GBM密切相关的核心基因,进一步利用TCGA肿瘤数据库验证,与芯片结果一致。
    结论 KEGG信号通路和核心基因可能揭示了多形性胶质母细胞瘤发生发展的分子机制,核心基因可能用作多形性胶质母细胞瘤的早期诊断的分子标志物和治疗靶点。

     

    Abstract:
    Objective To identify the hub genes and signal pathways of glioblastoma multiforme(GBM) by microarray and bioinformatics analysis method, and to find out the potential markers for early diagnosis and targeted therapy of GBM.
    Methods The expression profiling data of GBM was obtained from the GEO database. R software was used to screen differentially expressed genes (DEGs), and DEGs was annotated using DAVID online tools for GO ontology and KEGG signaling pathway enrichment. Moreover, protein-protein interaction network(PPI) was constructed and from which the hub genes were selected. Finally, the TCGA database was used to validate the hub genes.
    Results Samples Pearson correlation analysis showed that the expression profiling was reliable. Totally 2142 DEGs including 968 up-regulated genes and 1174 down-regulated genes were screened. GO and KEGG enrichment showed that the DEGs mainly correlated with cell cycle, cell division and proliferation, synaptic transmission and other biological functions and pathways. Pathway network analysis indicated that MAPK signal pathway played a core regulatory role in the network. In addition, 9 hub genes most related to GBM were screened from PPI network, and further confirmed by TCGA database.
    Conclusion KEGG signaling pathways and hub genes may reveal the molecular mechanism of the development of GBM, and the hub genes may be used as the molecular marker for early diagnosis and therapeutic targets of GBM.

     

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