Quantitative proteomics allows the characterization of molecular changes between healthy and disease states. To interpret such datasets, their integration to the protein-protein interaction network provides a more comprehensive understanding of cellular function dysregulation in diseases than just considering lists of dysregulated proteins. Here, we propose a novel computational method, which combines protein interaction network and statistical analyses to establish expression profiles at the network module level rather than at the individual protein level, and to detect and characterize dysregulated network modules through different stages of cancer progression. We applied our approach to two publicly available datasets as case studies.