Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework

Gaurav Kumar,Shoba Ranganathan,Edmond J Breen

doi:10.1186/1752-0509-7-12

Abstract

BackgroundCancer 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.ResultsWe 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.ConclusionWe 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.

Highlights

Cancer is a complex disease where molecular mechanism remains elusive
This study suggests that of the 17 shortlisted genes flagged as significant, the overexpressed genes IRAK1, CHEK1 and BUB1 may play an important role in ovarian cancer
We report three genes (IRAK1, CHEK1 and BUB1) to be significant in ovarian tumor samples for the first time, to the best of our knowledge

Summary

Introduction

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. The development of gene expression microarrays more than a decade ago has led to the study of changes in the mRNA transcripts in disease-related tissues These transcriptomic analyses from microarrays experiments served as the proxy for protein expression, and thereby revealed important properties of gene sets related to tissuespecificity [1,2]. The knowledge acquired over the years of research suggests that the cancer cells harbour genetic defects that alter the balance of cell proliferation and cell death [11] This has led to the compilation of a cancer gene list, which has increased steadily over the last two decades. In order to explore the combinatorial influence of multiple factors, Boolean-based logic is a popular approach for SNP association studies [13,14] and in cancer [12,15,16]

Methods

Results

Conclusion