Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA.
El Colegio de México, Tlalpan, Mexico City, 14110, Mexico.
Sci Rep. 2021 Aug 11;11(1):16284. doi: 10.1038/s41598-021-95313-y.
Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.
基因共表达网络（GCNs）已被开发为研究复杂表型背后基因表达模式的相关分析工具。确定 GCN 中结构与功能之间的关联是当前生物医学研究的一个挑战。已经报道了乳腺癌和健康表型的 GCN 之间存在几种结构差异。在之前的一项研究中，我们使用共表达多层网络表明，基底样乳腺癌的 GCN 中顶级共表达基因对和其余基因对之间的连接模式存在突然差异。在这里，我们比较了四种乳腺癌表型（Luminal-A、Luminal-B、Her2+和 Basal）的前 100,000 个相互作用网络的结构特性。为此，我们使用网络的图论 k-核（具有至少 k 度的节点的最大子网络）。我们开发了一种全面分析癌症中网络 k-核 ([Formula: see text]) 结构及其与生物学功能关系的方法。我们发现，在 Top-100,000-edges 网络中，乳腺癌网络中的大多数相互作用是在染色体内部，而染色体间相互作用则作为簇之间的连接桥。此外，健康网络中的核心基因与代谢和细胞周期等过程密切相关。在乳腺癌中，只有 Luminal A 的核心与这些过程相关，并且其核心中的基因过表达。所有乳腺癌亚型的核心节点的交集仅由 chr8q24.3 区域的基因组成。在此之前，已经观察到该区域在几种癌症中高度扩增，并且它出现在四种乳腺癌 k-核的交集，可能表明局部共表达是癌症中的一种保守现象。考虑到这些现象与许多错综复杂的因素以及目前正在进行的大量关于表观基因组调控的研究，需要进一步研究表观基因组对癌症中基因共表达网络的结构和功能的影响。
