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.

This project has been carried out with funding from Spanish Government (MICINN-SAF2010-21385 and BFU2017-87843-R to A.P.-M), a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme (PIOF-GA-2009-237120 to A.P.-M.), the Generalitat de Catalunya research program (AGAUR, 2014-SGR-1628 to A.P.-M.). P.D.-M. was the recipient of a FI fellowship from Generalitat de Catalunya (FI-2013FIB00251); A.P.-M. was the recipient of the Universitat Autònoma de Barcelona-Programa Banco de Santander Fellowship