Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

中文翻译：

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FUNMarker：基于融合网络的方法来识别预后和异质性乳腺癌生物标志物

乳腺癌是一种异质性疾病，具有许多临床上可区分的分子亚型，每个亚型对应于一组患者。乳腺癌预后和异质性生物标志物的鉴定是为了检测可用于准确预测乳腺癌预后的集群特异性基因生物标志物。在这项研究中，我们提出了一种基于 FUsion 网络的方法 (FUNMarker)，通过考虑患者样本的异质性和来自多个来源的生物信息来识别预后和异质性乳腺癌生物标志物。为了减少患者异质性的影响，首先使用基于基因表达主成分的K-means算法对样本进行聚类。对于每个聚类，综合评价基因对乳腺癌的影响，基因从三个方面进行加权：生物学功能、预后能力和与已知疾病基因的相关性。然后通过融合网络上的标签传播模型对它们进行排名，该模型结合了来自七种网络的物理蛋白质相互作用，从而可以减少相互作用组不完整的影响。我们将 FUNMarker 与三种最先进的方法进行了比较，结果表明，在区分具有不同预后结果的患者方面，FUNMarker 鉴定的生物标志物具有生物学可解释性，并且比现有方法具有更强的辨别力。然后通过融合网络上的标签传播模型对它们进行排名，该模型结合了来自七种网络的物理蛋白质相互作用，从而可以减少相互作用组不完整的影响。我们将 FUNMarker 与三种最先进的方法进行了比较，结果表明，在区分具有不同预后结果的患者方面，FUNMarker 鉴定的生物标志物具有生物学可解释性，并且比现有方法具有更强的辨别力。然后通过融合网络上的标签传播模型对它们进行排名，该模型结合了来自七种网络的物理蛋白质相互作用，从而可以减少相互作用组不完整的影响。我们将 FUNMarker 与三种最先进的方法进行了比较，结果表明，在区分具有不同预后结果的患者方面，FUNMarker 鉴定的生物标志物具有生物学可解释性，并且比现有方法具有更强的辨别力。