| 作者： | Miguel Angel Pujana, Jing-Dong J Han, Lea M Starita, Kristen N Stevens, Muneesh Tewari, Jin Sook Ahn, Gad Rennert, Víctor Moreno, Tomas Kirchhoff, Bert Gold, Volker Assmann, Wael M ElShamy, Jean-Fran？ois Rual, Douglas Levine, Laura S Rozek, Rebecca S Gelman, Kristin C Gunsalus, Roger A Greenberg, Bijan Sobhian, Nicolas Bertin, Kavitha Venkatesan, Nono Ayivi-Guedehoussou, Xavier Solé, Pilar Hernández, Conxi Lázaro, Katherine L Nathanson, Barbara L Weber, Michael E Cusick, David E Hill, Kenneth Offit, David M Livingston, Stephen B Gruber, Jeffrey D Parvin & Marc Vidal |
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| 刊物名称： | Nature Genetics |
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| 摘要： | 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. |
