Abstract
This study highlights the relevance of network-guided controllability analysis as a precision oncology tool. Target controllability through networks is potentially relevant to cancer research for the identification of therapeutic targets. With reference to a recent study on multiple phenotypes from 22 osteosarcoma (OS) cell lines characterized both in vitro and in vivo, we found that a variety of critical proteins in OS regulation circuits were in part phenotype specific and in part shared. To generalize our inference approach and match cancer phenotypic heterogeneity, we employed multitype networks and identified targets in correspondence with protein sub-complexes. Therefore, we established the relevance for diagnostic and therapeutic purposes of inspecting interactive targets, namely those enriched by significant connectivity patterns in protein sub-complexes. Emerging targets appeared with reference to the OS microenvironment, and relatively to small leucine-rich proteoglycan members and D-type cyclins, among other collagen, laminin, and keratin proteins. These described were evidences shared across all phenotypes; instead, specific evidences were provided by critical proteins including IGFBP7 and PDGFRA in the invasive phenotype, and FGFR3 and THBS1 in the colony forming phenotype.
Highlights
Introduction(c) What are the effects of disrupting network connectivity by acting over particular nodes?
In biological networks, control theory addresses questions such as (a) how we decompose the structure of a complex network into components to simplify their functional interpretability? (b) Can redundant nodes and links be reduced to guarantee better network performance? (c) What are the effects of disrupting network connectivity by acting over particular nodes?It would be useful to find a so-called network skeleton or core serving efficiently general inference purposes, possibly with no loss of information
Ip, and Cp states shared molecular functions related to extracellular matrix (ECM) structural constituents containing collagen-related differentially expressed genes (DEGs)
Summary
(c) What are the effects of disrupting network connectivity by acting over particular nodes?. It would be useful to find a so-called network skeleton or core serving efficiently general inference purposes, possibly with no loss of information. Such skeleton is expected to be significantly smaller than the network, while reproducing its characteristic properties. In an attempt to select subsets of nodes and links, controllability may involve the search of a minimum dominating set (MDS) [1]. Sets of the same size may differentiate by various node functional states, triggering a variety of connectivity paths and regulatory circuits
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