Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework
1 ARC Centre of Excellence in Bioinformatics and Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia
2 Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, 1112 East Clay, P.O. Box 980533, Richmond, VA, 23298, USA
3 Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, NSW, 2109, Australia
4 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597, Singapore
BMC Systems Biology 2013, 7:12 doi:10.1186/1752-0509-7-12Published: 6 February 2013
Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.
We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inactivation and seven genes are up-regulated. Here, we report that the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer. We also show that these 17 genes can be used to form an ovarian cancer signature, to distinguish normal from ovarian cancer subjects and that the set of three genes, CHEK1, AR, and LYN, can be used to classify good and poor prognostic tumors.
We provided a workflow using a Boolean logic schema for the identification of differentially expressed genes by integrating diverse biological information. This integrated approach resulted in the identification of genes as potential biomarkers in ovarian cancer.