2017
DOI: 10.1038/s41467-017-00268-2
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Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures

Abstract: Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network model… Show more

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Cited by 87 publications
(111 citation statements)
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“…Next, we selected the most relevant motifs for OL differentiation. To this end, we adapted the method used in Khan et al () and assembled the following features for each motif (m i ): (a) motif expression, Expr(m i ): average expression of the components of the motifs for each differentiation stage (i.e., OPC, iOL, or mOL); (b) motif centrality, Cent(m i ): average betweenness centrality of the nodes in the motif as calculated by NetworkAnalyzer in Cytoscape, (c) motif connectivity,Conn(m i ): average connectivity (incoming and outgoing edges) of the nodes in the motif as calculated by NetworkAnalyzer in Cytoscape. Next, we integrated all the features for each node of the motifs in the following weighted score function: Fi=w1Expr()normalmnormalimin()Expr()mmax()Expr()mmin()Expr()m+w2Cent()normalmnormalimin()Cent()mmax()Cent()mmin()Cent()m+w3Conn()normalmnormalimin()Conn()mmax()Conn()mmin()Conn()m …”
Section: Methodsmentioning
confidence: 99%
“…Next, we selected the most relevant motifs for OL differentiation. To this end, we adapted the method used in Khan et al () and assembled the following features for each motif (m i ): (a) motif expression, Expr(m i ): average expression of the components of the motifs for each differentiation stage (i.e., OPC, iOL, or mOL); (b) motif centrality, Cent(m i ): average betweenness centrality of the nodes in the motif as calculated by NetworkAnalyzer in Cytoscape, (c) motif connectivity,Conn(m i ): average connectivity (incoming and outgoing edges) of the nodes in the motif as calculated by NetworkAnalyzer in Cytoscape. Next, we integrated all the features for each node of the motifs in the following weighted score function: Fi=w1Expr()normalmnormalimin()Expr()mmax()Expr()mmin()Expr()m+w2Cent()normalmnormalimin()Cent()mmax()Cent()mmin()Cent()m+w3Conn()normalmnormalimin()Conn()mmax()Conn()mmin()Conn()m …”
Section: Methodsmentioning
confidence: 99%
“…To conceptually analyze and intuitively visualize interactions in such a vast regulatory network, the construction of a molecular interaction map is the first step in modern biological studies 19,20 . A number of comprehensive regulatory maps have been constructed to help in the understanding of the mechanisms underlying complex biological systems [21][22][23][24] , however, only a handful of attempts have been made so far to develop a mechanistic understanding of complex inflammatory disorders through the investigation of underlying molecular interaction maps [25][26][27][28] .…”
Section: Problem and Research Gapmentioning
confidence: 99%
“…species, cell types, genetics, environment) dynamical model to understand detailed insights of acute inflammation and resolution. While such systems biology approaches are already well established in cancer research 23 , acute inflammation and inflammation resolution offer plenty new opportunities for more interdisciplinary approaches using mathematical modelling and computer simulations. The AIR provides a valuable starting point to identify core regulatory networks that can be subjected to dynamic mechanistic modelling.…”
Section: Air As a Directed Graphmentioning
confidence: 99%
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“…The navigation interface include features such as scrolling, zooming, markers and callouts using Google Maps technology adapted by NaviCell [14] ( Figure 3), web-based platform supporting ACSN and similar efforts in CellDesigner format [15,16] or other formats [17]. The semantic zooming in NaviCell (http://navicell.curie.fr), provides several view levels, achieved by gradual exclusion of details and abstraction of information upon zooming out ( Figure 2B).…”
Section: Introductionmentioning
confidence: 99%