title: Differential network entropy reveals cancer system hallmarks.
abstract: The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets.
authors.affiliation: Statistical Cancer Genomics, UCL Cancer Institute, University College London, London, United Kingdom.
authors.forename: James
authors.initials: J
authors.lastname: West
authors.forename: Ginestra
authors.initials: G
authors.lastname: Bianconi
authors.forename: Simone
authors.initials: S
authors.lastname: Severini
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Teschendorff
mesh_terms: D002453:Cell Cycle / Q000235:genetics; D049109:Cell Proliferation; D019277:Entropy; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000378:metabolism* / Q000473:pathology; D060066:Protein Interaction Maps