A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer

BMC Systems Biology. 2011;5(1):35 DOI 10.1186/1752-0509-5-35


Journal Homepage

Journal Title: BMC Systems Biology

ISSN: 1752-0509 (Online)

Publisher: BMC

LCC Subject Category: Science: Biology (General)

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML



Reverter Antonio
Nagaraj Shivashankar H


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks


Abstract | Full Text

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 (<it>FXYD1</it>, <it>GUCA2B, REG3A</it>); 2) kinases as potential drug candidates to prevent tumor growth (<it>CDC42BPB, EPHB3, TRPM6</it>); and 3) potential oncogenic transcription factors (<it>CDK8</it>, <it>MEF2C, ZIC2</it>).</p> <p>Conclusion</p> <p>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.</p>