Analysis of extracellular matrix network dynamics in cancer using the MatriNet database
Analysis of extracellular matrix network dynamics in cancer using the MatriNet database
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DOI：
10.1016/j.matbio.2022.05.006
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发表时间：
2022-05-18
期刊：
影响因子：
6.9
通讯作者：
Izzi，Valerio
中科院分区：
文献类型：
--
作者：
Kontio，Juho;Sonora，Valeria Rolle;Izzi，Valerio
细胞外基质（ECM）是由多种性质的蛋白质组成的三维网络，它们之间的相互作用对于为组织提供正确的机械和生化信号至关重要，这些信号是组织正常发育和体内平衡所需要的。细胞外基质（ECM）成分的数量变化及其在肿瘤微环境（TME）内的平衡伴随并推动了肿瘤发展、生长和转移的所有步骤，对这些过程的更深入、更系统的理解是未来治疗方法发展的基础。来自众多来源的丰富的“大数据”使我们在理解致癌过程方面取得了巨大的进步，也影响了我们对TME中ECM变化的理解。然而，现有的大多数研究都没有考虑到ECM的网络性质，以及成分数量的变化可能在癌症中受到调节（共同发生），并通过其连接在整个网络上显着“反弹”，从根本上改变基质相互作用组的可能性。为了促进对这些网络规模效应的探索，我们已经实现了tedmatriinet (www.matrinet.org)，这是一个数据库，可以研究ECM网络架构的结构变化，作为它们在20种不同肿瘤类型中蛋白质-蛋白质相互作用强度的函数。使用matriinetis直观，并为肿瘤特异性和泛癌症的ECM网络特征提供了新的见解，有助于识别癌症之间的相似性和差异性，以及单个肿瘤事件的可视化和ECM靶点的优先级，以进行进一步的实验研究。
The extracellular matrix (ECM) is a three-dimensional network of proteins of diverse nature, whose interactions are essential to provide tissues with the correct mechanical and biochemical cues they need for proper development and homeostasis. Changes in the quantity of extracellular matrix (ECM) components and their balance within the tumor microenvironment (TME) accompany and fuel all steps of tumor development, growth and metastasis, and a deeper and more systematic understanding of these processes is fundamental for the development of future therapeutic approaches. The wealth of “big data” from numerous sources has enabled gigantic steps forward in the comprehension of the oncogenic process, also impacting on our understanding of ECM changes in the TME. Most of the available studies, however, have not considered the network nature of ECM and the possibility that changes in the quantity of components might be regulated (co-occur) in cancer and significantly “rebound” on the whole network through its connections, fundamentally altering the matrix interactome. To facilitate the exploration of these network-scale effects we have implementedMatriNet(www.matrinet.org), a database enabling the study of structural changes in ECM network architectures as a function of their protein-protein interaction strengths across 20 different tumor types. The use ofMatriNetis intuitive and offers new insights into tumor-specific as well as pan-cancer features of ECM networks, facilitating the identification of similarities and differences between cancers as well as the visualization of single-tumor events and the prioritization of ECM targets for further experimental investigations.