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Open Access Research article

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

Mandeep Kaur1, Cameron R MacPherson1, Sebastian Schmeier1, Kothandaraman Narasimhan2, Mahesh Choolani3 and Vladimir B Bajic1*

Author Affiliations

1 Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia

2 Centre for Excellence in Genomic Medicine Research, King Abdul Aziz University, PO. Box 80216, Jeddah 21589, Kingdom of Saudi Arabia

3 Diagnostic Biomarker Discovery Laboratory, Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University Health System, 5 Lower Kent Ridge Road, 119074, Singapore

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BMC Systems Biology 2011, 5:144  doi:10.1186/1752-0509-5-144

Published: 19 September 2011

Abstract

Background

Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.

Results

The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.

Conclusions

We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.