Show simple item record

dc.contributor.authorKaur, Mandeep
dc.contributor.authorMacPherson, Cameron R.
dc.contributor.authorSchmeier, Sebastian
dc.contributor.authorNarasimhan, Kothandaraman
dc.contributor.authorChoolani, Mahesh
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2014-08-27T09:43:39Z
dc.date.available2014-08-27T09:43:39Z
dc.date.issued2011-09-20
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.
dc.identifier.issn17520509
dc.identifier.pmid21923952
dc.identifier.doi10.1186/1752-0509-5-144
dc.identifier.urihttp://hdl.handle.net/10754/325266
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.
dc.language.isoen
dc.publisherSpringer Nature
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.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectdiagnostic agent
dc.subjectestrogen
dc.subjecttranscription factor
dc.subjecttumor marker
dc.subjectbinding site
dc.subjectbiology
dc.subjectdrug effect
dc.subjectevaluation
dc.subjectgene expression regulation
dc.subjectgenetics
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectovary tumor
dc.subjectphysiology
dc.subjectpromoter region
dc.subjecttumor gene
dc.subjectBinding Sites
dc.subjectComputational Biology
dc.subjectEstrogens
dc.subjectGene Expression Regulation, Neoplastic
dc.subjectGenes, Neoplasm
dc.subjectOvarian Neoplasms
dc.subjectPromoter Regions, Genetic
dc.subjectTranscription Factors
dc.subjectTumor Markers, Biological
dc.titleIn Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBMC Systems Biology
dc.identifier.pmcidPMC3184078
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCentre for Excellence in Genomic Medicine Research, King Abdul Aziz University, PO. Box 80216, Jeddah 21589, Saudi Arabia
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, Singapore
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personKaur, Mandeep
kaust.personMacPherson, Cameron R.
kaust.personSchmeier, Sebastian
kaust.personBajic, Vladimir B.
refterms.dateFOA2018-06-14T07:11:39Z
dc.date.published-online2011-09-20
dc.date.published-print2011


Files in this item

Thumbnail
Name:
Article-BMC_System-In_Silico_-2011.pdf
Size:
2.905Mb
Format:
PDF
Description:
Article - Full Text
Thumbnail
Name:
Supplement_1_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S1.XLS
Size:
42Kb
Format:
Microsoft Excel
Description:
Supplemental File 1
Thumbnail
Name:
Supplement_2_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S2.XLS
Size:
394.5Kb
Format:
Microsoft Excel
Description:
Supplemental File 2
Thumbnail
Name:
Supplement_3_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S3.TXT
Size:
413bytes
Format:
Text file
Description:
Supplemental File 3
Thumbnail
Name:
Supplement_4_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S4.TXT
Size:
591bytes
Format:
Text file
Description:
Supplemental File 4
Thumbnail
Name:
Supplement_5_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S5.TXT
Size:
578bytes
Format:
Text file
Description:
Supplemental File 5
Thumbnail
Name:
Supplement_6_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S6.XLSX
Size:
23.96Kb
Format:
Microsoft Excel 2007
Description:
Supplemental File 6
Thumbnail
Name:
Supplement_7_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S7.DOCX
Size:
33.95Kb
Format:
Microsoft Word 2007
Description:
Supplemental File 7
Thumbnail
Name:
Supplement_8_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S8.DOCX
Size:
135.8Kb
Format:
Microsoft Word 2007
Description:
Supplemental File 8
Thumbnail
Name:
Supplement_9_-_BMC_System-In_Silico_-2011.1752-0509-5-144-S9.DOCX
Size:
136.1Kb
Format:
Microsoft Word 2007
Description:
Supplemental File 9

This item appears in the following Collection(s)

Show simple item record

This 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.
Except where otherwise noted, this item's license is described as This 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.