The study of co-expression programs in the context of cancer can help to elucidate the genetic mechanisms that are altered and lead to the disease. The identification of gene co-expression patterns, unique to healthy profiles (and absent in cancer) is an important step in this direction. Networks are a good tool for achieving this as they allow to model local and global structural properties of the gene co-expression program. This is the case of gene co-expression networks (GCNs), where nodes or vertices represent genes and an edge between two nodes exists if the corresponding genes are co-expressed. Single threshold co-expression networks are often used for this purpose. However, important interactions in a broader co-expression space needed to unravel such mechanisms may be overlooked. In this work, we use a multilayer network approach that allows us to study co-expression as a discrete object, starting at weak levels of co-expression building itself upward towards the top co-expressing gene pairs.We use a multilayer GCNs (or simply GCNs), to compare healthy and breast cancer co-expression programs. By using the layers of the gene co-expression networks, we were able to identify a structural mechanism unique in the healthy GCN similar to well-known preferential attachment. We argue that this mechanism may be a reflection of an organizational principle that remains absent in the breast cancer co-expression program. By focusing on two well-defined set of nodes in the top co-expression layers of the GCNs—namely hubs and nodes in the main core of the network—we found a set of genes that is well conserved across the co-expression program. Specifically, we show that nodes with high inter-connectedness as opposed to high connectedness are conserved in the healthy GCN. This set of genes, we discuss, may partake in several different functional pathways in the regulatory program. Finally, we found that breast cancer GCN is composed of two different structural mechanisms, one that is random and is composed by most of the co-expression layers, and another non-random mechanism found only in the top co-expression layers.Overall, we are able to construct within this approach a portrait of the whole transcriptome co-expression program, thus providing a novel manner to study this complex biological phenomenon.

中文翻译：

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多层方法揭示了乳腺癌共表达网络中破坏的组织原理

在癌症背景下对共表达程序的研究可以帮助阐明被改变并导致疾病的遗传机制。健康共性（和癌症中不存在）所特有的基因共表达模式的鉴定是朝这个方向迈出的重要一步。网络是实现此目的的好工具，因为它们允许对基因共表达程序的局部和全局结构特性进行建模。这是基因共表达网络（GCN）的情况，其中节点或顶点代表基因，并且如果相应的基因被共表达，则两个节点之间存在一条边。单阈值共表达网络通常用于此目的。但是，在广泛的共表达空间中解开此类机制所需的重要交互作用可能会被忽略。在这项工作中 我们使用多层网络方法，使我们可以将共表达作为离散对象进行研究，从低水平的共表达开始，将自身构建为朝向顶部的共表达基因对。我们使用多层GCN（或简称为GCN），比较健康和乳腺癌的共表达程序。通过使用基因共表达网络的层，我们能够确定健康GCN中独特的结构机制，类似于众所周知的优先附着。我们认为这种机制可能反映了乳腺癌共表达程序中仍然缺乏的组织原则。通过关注GCN顶级共表达层中两个定义明确的节点集（即网络主核心中的集线器和节点），我们发现了在整个共表达程序中保存良好的一组基因。具体而言，我们表明健康的GCN中保留了具有高互连度而不是高连通性的节点。我们讨论的这组基因可能参与调节程序中的几种不同功能途径。最后，我们发现乳腺癌GCN由两种不同的结构机制组成，一种是随机的并且由大多数共表达层组成，另一种仅在顶层共表达层中存在的非随机机制。我们能够用这种方法构建整个转录组共表达程序的肖像，从而提供一种新颖的方式来研究这种复杂的生物学现象。可能参与监管计划中的几种不同功能途径。最后，我们发现乳腺癌GCN由两种不同的结构机制组成，一种是随机的并且由大多数共表达层组成，另一种仅在顶层共表达层中存在的非随机机制。我们能够在这种方法下构建整个转录组共表达程序的肖像，从而提供一种新颖的方式来研究这种复杂的生物学现象。可能参与监管计划中的几种不同功能途径。最后，我们发现乳腺癌GCN由两种不同的结构机制组成，一种是随机的并且由大多数共表达层组成，另一种仅在顶层共表达层中存在的非随机机制。我们能够在这种方法下构建整个转录组共表达程序的肖像，从而提供一种新颖的方式来研究这种复杂的生物学现象。