Hidalgo, Marta R.Amadoz, AliciaÇubut, CankutCarbonell-Caballero, JoseDopazo, Joaquín2019-05-302019-05-302018Hidalgo MR, Amadoz A, Çubuk C, Carbonell-Caballero J, Dopazo J. Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome. Biol Direct. 2018; 13(1):16. DOI 10.1186/s13062-018-0219-41745-6150http://hdl.handle.net/10230/41660Background: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases. Results: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression. Conclusion: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker. Reviewers: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.application/pdfeng© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13062-018-0219-4info:eu-repo/semantics/openAccess