Zhang Yuji, Xuan Jianhua, de Los Reyes Benilo G, Clarke Robert, Ressom Habtom W
Department of Electrical and Computer Engineering, Advanced Research Institute, Virginia Polytechnic Institute and State University, 4300 Wilson Blvd, Arlington, 22203, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:5696-9. doi: 10.1109/IEMBS.2008.4650507.
Identifying breast cancer susceptibility genes is one of the key challenges in breast cancer research. Conventional gene-based approaches can identify patterns of gene activity that sub-classify tumors, by which genes with known breast cancer mutations are typically not detected. In this study, we present a novel network motif-based approach that integrates biological network topology and high-throughput gene expression data to identify markers not as individual genes but as network motifs. We observed that the network motifs are more reproducible than individual marker genes selected without biological network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
识别乳腺癌易感基因是乳腺癌研究中的关键挑战之一。传统的基于基因的方法可以识别对肿瘤进行亚分类的基因活性模式，而通过这种方法通常无法检测到具有已知乳腺癌突变的基因。在本研究中，我们提出了一种基于网络基序的新方法，该方法整合了生物网络拓扑结构和高通量基因表达数据，以识别作为网络基序而非单个基因的标志物。我们观察到，与在没有生物网络信息的情况下选择的单个标志物基因相比，网络基序具有更高的可重复性，并且在转移性肿瘤与非转移性肿瘤的分类中具有更高的准确性。
