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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >A network-based approach to enrich gene signatures for the prediction of breast cancer metastases
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A network-based approach to enrich gene signatures for the prediction of breast cancer metastases

机译：基于网络的富集基因特征来预测乳腺癌转移

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Despite the multiplicity of the gene expression analysis studies for the identification of genomics based origins of cancerous diseases, the presented gene signatures have generally little overlap. The genes do not function in isolation and therefore a more holistic approach that takes into account the interactions among them is needed. In this study we present a stepwise refinement methodology where starting from some initial set of biomarkers we expand and enrich this set taking into account existing biological information. In particular, we start with a 27 gene signature previously identified as indicative of the presence of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients. We use the manually curated HINT database of protein-protein interactions as the background biological network to locate the network-based similarity of the input genes and how they connect to each other. The result is an enriched connected set of genes that is subsequently expanded to form an even bigger network based on the ability of the surrounding genes to strongly correlate with the phenotypes of a training set of breast cancer patient cases. The induced network is then used as a new gene signature for the classification of breast brain metastases in an independent dataset. The results are encouraging for the validity of this method.
机译：尽管基因表达分析的多样性，用于鉴定基于基因组学的癌症疾病的起源，所呈现的基因特征通常几乎没有重叠。该基因不能以隔离起作用，因此需要一种更全面的方法，以考虑它们之间的相互作用。在这项研究中，我们提出了一种逐步改进方法，其中从一些初始生物标志物开始，我们展开并丰富了本集合考虑到现有的生物信息。特别是，我们从先前鉴定为27个基因签名，该签名是指示乳腺癌患者外周血中循环肿瘤细胞（CTC）的存在。我们使用蛋白质 - 蛋白质相互作用的手动愈合的提示数据库作为背景生物网络来定位输入基因的基于网络的相似性以及它们如何彼此连接。结果是一种富集的基因组，随后扩展以基于周围基因与乳腺癌患者病例的训练集的表型强烈相关的能力，形成甚至更大的网络。然后将诱导的网络用作独立数据集中乳腺脑转移的分类的新基因签名。结果令人鼓舞了这种方法的有效性。

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