A TRPV2 interactome-based signature for prognosis in glioblastoma patients
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Pau Doñate-Macián1,*, Antonio Gómez2,3,*, Irene R. Dégano4,5 and Alex Perálvarez-Marín1
1Unitat de Biofísica, Departament de Bioquímica i de Biologia Molecular, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain
2Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
3Universitat Pompeu Fabra, (UPF), Barcelona, Catalonia, Spain
4CIBER Cardiovascular Diseases (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
5REGICOR Study Group, Cardiovascular Epidemiology and Genetics Group, IMIM (Hospital Del Mar Medical Research Institute), Barcelona, Catalonia, Spain
*These authors contributed equally to this work
Alex Perálvarez-Marín, email: [email protected]
Keywords: TRPV2; proteomics; glioblastoma multiforme; gene-disease associations; gene signature
Received: July 02, 2017 Accepted: March 01, 2018 Published: April 06, 2018
Proteomics aids to the discovery and expansion of protein-protein interaction networks, which are key to understand molecular mechanisms in physiology and physiopathology, but also to infer protein function in a guilt-by-association fashion. In this study we use a systematic protein-protein interaction membrane yeast two-hybrid method to expand the interactome of TRPV2, a cation channel related to nervous system development. After validation of the interactome in silico, we define a TRPV2-interactome signature combining proteomics with the available physio-pathological data in Disgenet to find interactome-disease associations, highlighting nervous system disorders and neoplasms. The TRPV2-interactome signature against available experimental data is capable of discriminating overall risk in glioblastoma multiforme prognosis, progression, recurrence, and chemotherapy resistance. Beyond the impact on glioblastoma physiopathology, this study shows that combining systematic proteomics with in silico methods and available experimental data is key to open new perspectives to define novel biomarkers for diagnosis, prognosis and therapeutics in disease.
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