Comprehensive Network Analysis of Cancer Stem Cell Signalling through Systematic Integration of Post-Translational Modification Dynamics

Abstract

Post‐translational modifications, such as phosphorylation, acetylation and ubiquitina-tion, are widely known to play various important roles in cellular signalling. Recent significant advances in mass spectrometry‐based proteomics technology enable us not only to comprehensively identify expressed proteins but also to unveil their post‐trans-lational modifications with high sensitivity. In our advanced proteome bioinformatics frameworks, statistical network analyses of large‐scale information on various post‐ translational modification dynamics were conducted to define the key machinery for cancer stem cell properties. The bioinformatical approaches using IPA (ingenuity path-way analysis), NetworKIN and a newly developed platform named PTMapper (post‐ translational modification mapper) allowed us to perform network‐wide prediction of upstream interactors/kinases with the related information on the diseases and functions, leading to systematic finding of novel drug candidates to regulate aberrant signalling in cancer stem cells. In this chapter, we apply patient‐derived glioblastoma stem cells as a representative model of cancer stem cells to introduce some useful platforms for statisti-cal and mathematical network analyses based on the large‐scale phosphoproteome data.