Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.
Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.
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基因调控网络分析揭示与多形性胶质母细胞瘤生存相关的核心基因
DOI：
10.1111/jcmm.15615
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发表时间：
2020-09
影响因子：
5.3
通讯作者：
Lv K
中科院分区：
文献类型：
--
作者：
Jiang L;Zhong M;Chen T;Zhu X;Yang H;Lv K
多形性胶质母细胞瘤（GBM）是一种死亡率很高的中枢神经系统肿瘤。选择来自GSE 2223、GSE 4058、GSE 4290、GSE 13276、GSE 68848和GSE 70231的微阵列数据（389个GBM肿瘤和67个正常组织）和来自TCGA-GBM数据集的RNA-seq数据（169个GBM和5个正常样品）以寻找差异表达基因（DEG）。使用RRA（Robust rank aggregation）方法整合7个数据集并计算133个DEG（82个上调基因和51个下调基因）。随后，通过PPI（蛋白质-蛋白质相互作用）网络和MCODE/ cytoHubba方法，我们最终从整个网络中筛选出FOXM 1、CDK 4、TOP 2A、RRM 2、MYBL 2、MCM 2、CDC 20、CCNB 2、MYC和EZH 2等10个hub基因。对DEG进行了功能富集分析，以显示这些枢纽基因在各种癌症相关功能和途径中显著富集。我们还选择了CCNB 2、CDC 20和MYBL 2作为核心生物标志物，并在CGGA、HPA和CCLE数据库中进一步验证，提示这三个核心hub基因可能参与GBM的起源。这些潜在的GBM生物标志物可能有助于阐明肿瘤发生的分子机制在GBM癌的诊断、预后和靶向治疗中的重要作用。
Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223, GSE4058, GSE4290, GSE13276, GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.
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影响因子：
5.2
作者：
Madhavan, Subha;Zenklusen, Jean-Claude;Kotliarov, Yuri;Sahni, Himanso;Fine, Howard A.;Buetow, Kenneth
通讯作者：
Buetow, Kenneth
DOI：
10.18632/aging.101415
发表时间：
2018-04-16
期刊：
Aging
影响因子：
--
作者：
Jia D;Li S;Li D;Xue H;Yang D;Liu Y
通讯作者：
Liu Y
影响因子：
3.4
作者：
Falzone, Luca;Romano, Giovanni L.;Candido, Saverio
通讯作者：
Candido, Saverio
影响因子：
3.3
作者：
Li, Jue-hui;He, Zhen-qiang;Mou, Yong-gao
通讯作者：
Mou, Yong-gao
急性淋巴细胞白血病糖皮质激素耐药相关关键候选基因和通路的综合生物信息学分析
影响因子：
4
作者：
Chen, Yanxin;Jiang, Peifang;Hu, Jianda
通讯作者：
Hu, Jianda