Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.
Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.
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DOI：
10.1158/1055-9965.epi-14-1270
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Pharoah PD
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作者：
Kar SP;Tyrer JP;Li Q;Lawrenson K;Aben KK;Anton-Culver H;Antonenkova N;Chenevix-Trench G;Australian Cancer Study;Australian Ovarian Cancer Study Group;Baker H;Bandera EV;Bean YT;Beckmann MW;Berchuck A;Bisogna M;Bjørge L;Bogdanova N;Brinton L;Brooks-Wilson A;Butzow R;Campbell I;Carty K;Chang-Claude J;Chen YA;Chen Z;Cook LS;Cramer D;Cunningham JM;Cybulski C;Dansonka-Mieszkowska A;Dennis J;Dicks E;Doherty JA;Dörk T;du Bois A;Dürst M;Eccles D;Easton DF;Edwards RP;Ekici AB;Fasching PA;Fridley BL;Gao YT;Gentry-Maharaj A;Giles GG;Glasspool R;Goode EL;Goodman MT;Grownwald J;Harrington P;Harter P;Hein A;Heitz F;Hildebrandt MA;Hillemanns P;Hogdall E;Hogdall CK;Hosono S;Iversen ES;Jakubowska A;Paul J;Jensen A;Ji BT;Karlan BY;Kjaer SK;Kelemen LE;Kellar M;Kelley J;Kiemeney LA;Krakstad C;Kupryjanczyk J;Lambrechts D;Lambrechts S;Le ND;Lee AW;Lele S;Leminen A;Lester J;Levine DA;Liang D;Lissowska J;Lu K;Lubinski J;Lundvall L;Massuger L;Matsuo K;McGuire V;McLaughlin JR;McNeish IA;Menon U;Modugno F;Moysich KB;Narod SA;Nedergaard L;Ness RB;Nevanlinna H;Odunsi K;Olson SH;Orlow I;Orsulic S;Weber RP;Pearce CL;Pejovic T;Pelttari LM;Permuth-Wey J;Phelan CM;Pike MC;Poole EM;Ramus SJ;Risch HA;Rosen B;Rossing MA;Rothstein JH;Rudolph A;Runnebaum IB;Rzepecka IK;Salvesen HB;Schildkraut JM;Schwaab I;Shu XO;Shvetsov YB;Siddiqui N;Sieh W;Song H;Southey MC;Sucheston-Campbell LE;Tangen IL;Teo SH;Terry KL;Thompson PJ;Timorek A;Tsai YY;Tworoger SS;van Altena AM;Van Nieuwenhuysen E;Vergote I;Vierkant RA;Wang-Gohrke S;Walsh C;Wentzensen N;Whittemore AS;Wicklund KG;Wilkens LR;Woo YL;Wu X;Wu A;Yang H;Zheng W;Ziogas A;Sellers TA;Monteiro AN;Freedman ML;Gayther SA;Pharoah PD
迄今为止，全基因组关联研究（GWAS）已经报道了12个与浆液性上皮性卵巢癌（EOC）风险相关的位点。我们假设这些基因座中的一些通过附近的转录因子（TF）基因起作用，并且通过共表达确定的这些TF的假定靶基因也可能与其他EOC风险相关。我们选择了12个全基因组显著风险位点顶部信号1mb以内的TF基因。相互信息（一种相关性形式）用于构建与来自the Cancer Genome Atlas的489个浆液性EOC肿瘤的统一微阵列数据集中每个选定的TF基因强共表达的基因网络。随后，根据严重EOC GWAS荟萃分析（2196例/ 4396例对照）的种系snp结果，使用基因水平测试对该数据集中代表的基因进行排名。基因集富集分析发现，6个以TF基因为中心的网络（HOXB2、HOXB5、HOXB6、HOXB7位于17q21.32， HOXD1、HOXD3位于2q31）在风险相关端显著富集（P<0.05， FDR<0.05）。通过独立关联研究（7,035例/21,693例对照），这些结果被重复（P<0.05）。六个网络中潜在富集的基因汇集成一个组合网络。我们发现了一个与严重EOC风险相关的以hox为中心的网络，其中包含几个在严重EOC发展中已知或新出现的基因。网络分析整合了大型的、特定于环境的数据集，有可能提供癌症易感性的机制见解，并优先考虑实验表征的基因。
Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co-expression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly co-expressed with each selected TF gene in the unified microarray data set of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this data set were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P<0.05 and FDR<0.05). These results were replicated (P<0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific data sets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization.