title: A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer.
abstract: BACKGROUND
  Cancer has remarkable complexity at the molecular level, with multiple genes, proteins, pathways and regulatory interconnections being affected. We introduce a systems biology approach to study cancer that formally integrates the available genetic, transcriptomic, epigenetic and molecular knowledge on cancer biology and, as a proof of concept, we apply it to colorectal cancer.
  
  
  RESULTS
  We first classified all the genes in the human genome into cancer-associated and non-cancer-associated genes based on extensive literature mining. We then selected a set of functional attributes proven to be highly relevant to cancer biology that includes protein kinases, secreted proteins, transcription factors, post-translational modifications of proteins, DNA methylation and tissue specificity. These cancer-associated genes were used to extract 'common cancer fingerprints' through these molecular attributes, and a Boolean logic was implemented in such a way that both the expression data and functional attributes could be rationally integrated, allowing for the generation of a guilt-by-association algorithm to identify novel cancer-associated genes. Finally, these candidate genes are interlaced with the known cancer-related genes in a network analysis aimed at identifying highly conserved gene interactions that impact cancer outcome. We demonstrate the effectiveness of this approach using colorectal cancer as a test case and identify several novel candidate genes that are classified according to their functional attributes. These genes include the following: 1) secreted proteins as potential biomarkers for the early detection of colorectal cancer (FXYD1, GUCA2B, REG3A); 2) kinases as potential drug candidates to prevent tumor growth (CDC42BPB, EPHB3, TRPM6); and 3) potential oncogenic transcription factors (CDK8, MEF2C, ZIC2).
  
  
  CONCLUSIONS
  We argue that this is a holistic approach that faithfully mimics cancer characteristics, efficiently predicts novel cancer-associated genes and has universal applicability to the study and advancement of cancer research.
authors.affiliation: Computational and Systems Biology, Commonwealth Scientific and Industrial Research Organisation, Division of Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St, Lucia, Brisbane, Queensland 4067, Australia.
authors.forename: Shivashankar H
authors.initials: SH
authors.lastname: Nagaraj
authors.forename: Antonio
authors.initials: A
authors.lastname: Reverter
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D015179:Colorectal Neoplasms / Q000235:genetics*; D019175:DNA Methylation; D053263:Gene Regulatory Networks / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D009928:Organ Specificity; D000073718:Pancreatitis-Associated Proteins; D011494:Protein Kinases / Q000378:metabolism; D049490:Systems Biology / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor; D000073718:Pancreatitis-Associated Proteins; C000612141:REG3A protein, human; D014157:Transcription Factors; D011494:Protein Kinases