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

Nagaraj, Shivashankar H. and Reverter, Antonio (2011) A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer. BMC Systems Biology, 5 : 35.1-35.15.


Author Nagaraj, Shivashankar H.
Reverter, Antonio
Title A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer
Journal name BMC Systems Biology  (ERA 2012 Listed)    (ERA 2010 Rank B)   Check publisher's open access policy
Publication date 2011-02-26
Sub-type Article
DOI 10.1186/1752-0509-5-35
Volume number 5
ISSN 1752-0509
Start page 35.1
End page 35.15
Total pages 15
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2012
Language eng
Formatted 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).
Conclusion: 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.
Keyword Dna methylation
Transcription factors
Prostate-cancer
Expression data
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Article number 35, pp. 1-15

Document type: Journal Article
Sub-type: Article
Collections: Non HERDC
Institute for Molecular Bioscience - Publications
 
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Created: Wed, 21 Mar 2012, 09:22:49 EST by Susan Allen on behalf of Institute for Molecular Bioscience