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

Handle URI:
http://hdl.handle.net/10754/325266
Title:
In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer
Authors:
Kaur, Mandeep; MacPherson, Cameron R; Schmeier, Sebastian; Narasimhan, Kothandaraman; Choolani, Mahesh; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
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. 2011 Kaur et al; licensee BioMed Central Ltd.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Kaur M, MacPherson CR, Schmeier S, Narasimhan K, Choolani M, et al. (2011) In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer. BMC Systems Biology 5: 144. doi:10.1186/1752-0509-5-144.
Publisher:
BioMed Central
Journal:
BMC Systems Biology
Issue Date:
19-Sep-2011
DOI:
10.1186/1752-0509-5-144
PubMed ID:
21923952
PubMed Central ID:
PMC3184078
Type:
Article
ISSN:
17520509
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorKaur, Mandeepen
dc.contributor.authorMacPherson, Cameron Ren
dc.contributor.authorSchmeier, Sebastianen
dc.contributor.authorNarasimhan, Kothandaramanen
dc.contributor.authorChoolani, Maheshen
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2014-08-27T09:43:39Z-
dc.date.available2014-08-27T09:43:39Z-
dc.date.issued2011-09-19en
dc.identifier.citationKaur M, MacPherson CR, Schmeier S, Narasimhan K, Choolani M, et al. (2011) In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer. BMC Systems Biology 5: 144. doi:10.1186/1752-0509-5-144.en
dc.identifier.issn17520509en
dc.identifier.pmid21923952en
dc.identifier.doi10.1186/1752-0509-5-144en
dc.identifier.urihttp://hdl.handle.net/10754/325266en
dc.description.abstractBackground: 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. 2011 Kaur et al; licensee BioMed Central Ltd.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subjectdiagnostic agenten
dc.subjectestrogenen
dc.subjecttranscription factoren
dc.subjecttumor markeren
dc.subjectbinding siteen
dc.subjectbiologyen
dc.subjectdrug effecten
dc.subjectevaluationen
dc.subjectgene expression regulationen
dc.subjectgeneticsen
dc.subjectmetabolismen
dc.subjectmethodologyen
dc.subjectovary tumoren
dc.subjectphysiologyen
dc.subjectpromoter regionen
dc.subjecttumor geneen
dc.subjectBinding Sitesen
dc.subjectComputational Biologyen
dc.subjectEstrogensen
dc.subjectGene Expression Regulation, Neoplasticen
dc.subjectGenes, Neoplasmen
dc.subjectOvarian Neoplasmsen
dc.subjectPromoter Regions, Geneticen
dc.subjectTranscription Factorsen
dc.subjectTumor Markers, Biologicalen
dc.titleIn Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian canceren
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBMC Systems Biologyen
dc.identifier.pmcidPMC3184078en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionCentre for Excellence in Genomic Medicine Research, King Abdul Aziz University, PO. Box 80216, Jeddah 21589, Saudi Arabiaen
dc.contributor.institutionDiagnostic Biomarker Discovery Laboratory, Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University Health System, 5 Lower Kent Ridge Road, 119074, Singaporeen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKaur, Mandeepen
kaust.authorMacPherson, Cameron R.en
kaust.authorSchmeier, Sebastianen
kaust.authorBajic, Vladimir B.en

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