Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis.
Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis.
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通过综合生物信息学分析鉴定多形性胶质母细胞瘤的核心基因及筛选潜在靶点
DOI：
10.3389/fonc.2020.615976
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发表时间：
2020
影响因子：
4.7
通讯作者：
Yang Q
中科院分区：
文献类型：
--
作者：
Yang J;Yang Q
多形性胶质母细胞瘤是颅内最常见的原发性恶性肿瘤，其病因和发病机制尚不清楚。随着人类基因组研究的不断深入，基于核心分子的胶质瘤亚型筛选研究也越来越深入。本研究通过对基因表达数据库（Gene Expression Omnibus，GEO）中的多形性胶质母细胞瘤（GBM）数据集GSE 90598、肿瘤基因组图谱（Cancer Genome Atlas，TCGA）中的多形性胶质母细胞瘤（GBM）数据集TCGA-GBM和低级别胶质瘤（LGG）数据集TCGA-LGG进行再分析，筛选出差异表达基因（DEG）。共发现150个交叉DEG，其中48个上调，102个下调。采用过代表性方法对GSE 90598数据集的DEG进行了富集，富集的多个GO功能项与神经细胞信号转导显著相关。通过基因集富集分析（GSEA）分析GBM和LGG之间的DEG，并显著富集参与突触信号和催产素信号通路的京都基因和基因组百科全书（KEGG）通路。然后，蛋白质-蛋白质相互作用（PPI）网络的构建，以评估由DEG编码的蛋白质的相互作用。MCODE从PPI网络中确定了2个模块。在模块1中具有最高程度的11个基因被指定为核心分子，即GABRD、KCNC 1、KCNA 1、SYT 1、CACNG 3、OPALIN、CD 163、HPCAL 4、ANK 3、KIF 5A和MS 4A 6A，它们主要富集在离子信号相关通路中。GSE 83300数据集的生存分析验证了11个核心基因的表达水平与生存之间的显著关系。最后，通过超几何检验对GBM和DrugBank数据库的核心分子进行评估，以确定10种与癌症和神经精神疾病相关的药物，包括四氯十氧化物。这些核心基因在诊断、预后和靶向治疗中的潜力以及离子信号通路与神经精神疾病和神经肿瘤之间的关系有待进一步研究。
Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.
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影响因子：
30.8
作者：
Melin BS;Barnholtz-Sloan JS;Wrensch MR;Johansen C;Il'yasova D;Kinnersley B;Ostrom QT;Labreche K;Chen Y;Armstrong G;Liu Y;Eckel-Passow JE;Decker PA;Labussière M;Idbaih A;Hoang-Xuan K;Di Stefano AL;Mokhtari K;Delattre JY;Broderick P;Galan P;Gousias K;Schramm J;Schoemaker MJ;Fleming SJ;Herms S;Heilmann S;Nöthen MM;Wichmann HE;Schreiber S;Swerdlow A;Lathrop M;Simon M;Sanson M;Andersson U;Rajaraman P;Chanock S;Linet M;Wang Z;Yeager M;GliomaScan Consortium;Wiencke JK;Hansen H;McCoy L;Rice T;Kosel ML;Sicotte H;Amos CI;Bernstein JL;Davis F;Lachance D;Lau C;Merrell RT;Shildkraut J;Ali-Osman F;Sadetzki S;Scheurer M;Shete S;Lai RK;Claus EB;Olson SH;Jenkins RB;Houlston RS;Bondy ML
通讯作者：
Bondy ML
DOI：
10.1111/j.1601-183x.2009.00515.x
发表时间：
2009-11
期刊：
Genes, brain, and behavior
影响因子：
--
作者：
Lamprecht R;Dracheva S;Assoun S;LeDoux JE
通讯作者：
LeDoux JE
影响因子：
9.2
作者：
Jiang W;Yang W;Yang W;Zhang J;Pang D;Gan L;Luo L;Fan Y;Liu Y;Chen M
通讯作者：
Chen M
DOI：
10.1056/nejmoa1402121
发表时间：
2015-06-25
期刊：
The New England journal of medicine
影响因子：
--
作者：
Cancer Genome Atlas Research Network;Brat DJ;Verhaak RG;Aldape KD;Yung WK;Salama SR;Cooper LA;Rheinbay E;Miller CR;Vitucci M;Morozova O;Robertson AG;Noushmehr H;Laird PW;Cherniack AD;Akbani R;Huse JT;Ciriello G;Poisson LM;Barnholtz-Sloan JS;Berger MS;Brennan C;Colen RR;Colman H;Flanders AE;Giannini C;Grifford M;Iavarone A;Jain R;Joseph I;Kim J;Kasaian K;Mikkelsen T;Murray BA;O'Neill BP;Pachter L;Parsons DW;Sougnez C;Sulman EP;Vandenberg SR;Van Meir EG;von Deimling A;Zhang H;Crain D;Lau K;Mallery D;Morris S;Paulauskis J;Penny R;Shelton T;Sherman M;Yena P;Black A;Bowen J;Dicostanzo K;Gastier-Foster J;Leraas KM;Lichtenberg TM;Pierson CR;Ramirez NC;Taylor C;Weaver S;Wise L;Zmuda E;Davidsen T;Demchok JA;Eley G;Ferguson ML;Hutter CM;Mills Shaw KR;Ozenberger BA;Sheth M;Sofia HJ;Tarnuzzer R;Wang Z;Yang L;Zenklusen JC;Ayala B;Baboud J;Chudamani S;Jensen MA;Liu J;Pihl T;Raman R;Wan Y;Wu Y;Ally A;Auman JT;Balasundaram M;Balu S;Baylin SB;Beroukhim R;Bootwalla MS;Bowlby R;Bristow CA;Brooks D;Butterfield Y;Carlsen R;Carter S;Chin L;Chu A;Chuah E;Cibulskis K;Clarke A;Coetzee SG;Dhalla N;Fennell T;Fisher S;Gabriel S;Getz G;Gibbs R;Guin R;Hadjipanayis A;Hayes DN;Hinoue T;Hoadley K;Holt RA;Hoyle AP;Jefferys SR;Jones S;Jones CD;Kucherlapati R;Lai PH;Lander E;Lee S;Lichtenstein L;Ma Y;Maglinte DT;Mahadeshwar HS;Marra MA;Mayo M;Meng S;Meyerson ML;Mieczkowski PA;Moore RA;Mose LE;Mungall AJ;Pantazi A;Parfenov M;Park PJ;Parker JS;Perou CM;Protopopov A;Ren X;Roach J;Sabedot TS;Schein J;Schumacher SE;Seidman JG;Seth S;Shen H;Simons JV;Sipahimalani P;Soloway MG;Song X;Sun H;Tabak B;Tam A;Tan D;Tang J;Thiessen N;Triche T Jr;Van Den Berg DJ;Veluvolu U;Waring S;Weisenberger DJ;Wilkerson MD;Wong T;Wu J;Xi L;Xu AW;Yang L;Zack TI;Zhang J;Aksoy BA;Arachchi H;Benz C;Bernard B;Carlin D;Cho J;DiCara D;Frazer S;Fuller GN;Gao J;Gehlenborg N;Haussler D;Heiman DI;Iype L;Jacobsen A;Ju Z;Katzman S;Kim H;Knijnenburg T;Kreisberg RB;Lawrence MS;Lee W;Leinonen K;Lin P;Ling S;Liu W;Liu Y;Liu Y;Lu Y;Mills G;Ng S;Noble MS;Paull E;Rao A;Reynolds S;Saksena G;Sanborn Z;Sander C;Schultz N;Senbabaoglu Y;Shen R;Shmulevich I;Sinha R;Stuart J;Sumer SO;Sun Y;Tasman N;Taylor BS;Voet D;Weinhold N;Weinstein JN;Yang D;Yoshihara K;Zheng S;Zhang W;Zou L;Abel T;Sadeghi S;Cohen ML;Eschbacher J;Hattab EM;Raghunathan A;Schniederjan MJ;Aziz D;Barnett G;Barrett W;Bigner DD;Boice L;Brewer C;Calatozzolo C;Campos B;Carlotti CG Jr;Chan TA;Cuppini L;Curley E;Cuzzubbo S;Devine K;DiMeco F;Duell R;Elder JB;Fehrenbach A;Finocchiaro G;Friedman W;Fulop J;Gardner J;Hermes B;Herold-Mende C;Jungk C;Kendler A;Lehman NL;Lipp E;Liu O;Mandt R;McGraw M;Mclendon R;McPherson C;Neder L;Nguyen P;Noss A;Nunziata R;Ostrom QT;Palmer C;Perin A;Pollo B;Potapov A;Potapova O;Rathmell WK;Rotin D;Scarpace L;Schilero C;Senecal K;Shimmel K;Shurkhay V;Sifri S;Singh R;Sloan AE;Smolenski K;Staugaitis SM;Steele R;Thorne L;Tirapelli DP;Unterberg A;Vallurupalli M;Wang Y;Warnick R;Williams F;Wolinsky Y;Bell S;Rosenberg M;Stewart C;Huang F;Grimsby JL;Radenbaugh AJ;Zhang J
通讯作者：
Zhang J
影响因子：
30.8
作者：
通讯作者：
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