Pujana Miguel Angel, Han Jing-Dong J, Starita Lea M, Stevens Kristen N, Tewari Muneesh, Ahn Jin Sook, Rennert Gad, Moreno Víctor, Kirchhoff Tomas, Gold Bert, Assmann Volker, Elshamy Wael M, Rual Jean-François, Levine Douglas, Rozek Laura S, Gelman Rebecca S, Gunsalus Kristin C, Greenberg Roger A, Sobhian Bijan, Bertin Nicolas, Venkatesan Kavitha, Ayivi-Guedehoussou Nono, Solé Xavier, Hernández Pilar, Lázaro Conxi, Nathanson Katherine L, Weber Barbara L, Cusick Michael E, Hill David E, Offit Kenneth, Livingston David M, Gruber Stephen B, Parvin Jeffrey D, Vidal Marc
Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, 44 Binney St., Boston, Massachusetts 02115, USA.
Nat Genet. 2007 Nov;39(11):1338-49. doi: 10.1038/ng.2007.2. Epub 2007 Oct 7.
Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.
为了阐明癌症易感性和进展的潜在分子机制，许多与癌症相关的基因仍有待鉴定。对于癌症基因中的突变如何在复杂的细胞网络背景下影响其产物，也需要有更深入的了解。在这里，我们使用了一种网络建模策略来鉴定可能与乳腺癌高风险相关的基因。从四个已知的编码乳腺癌肿瘤抑制因子的基因开始，我们将基因表达谱分析与来自各种物种的功能基因组和蛋白质组（或“组学”）数据相结合，生成了一个包含118个基因的网络，这些基因由866个潜在的功能关联连接。该网络显示出比随机预期更高的连通性，这表明其组成部分在生物学相关途径中发挥作用。该网络的一个组成部分是HMMR，它编码一种中心体亚基，我们证明了它与乳腺癌相关基因BRCA1存在以前未知的功能关联。两项关于新发乳腺癌的病例对照研究表明，HMMR基因座与人类乳腺癌的高风险相关。我们的网络建模策略应该有助于发现更多与癌症相关的基因。
