title: In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer.
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.
authors.affiliation: Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
authors.forename: Mandeep
authors.initials: M
authors.lastname: Kaur
authors.forename: Cameron R
authors.initials: CR
authors.lastname: MacPherson
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schmeier
authors.forename: Kothandaraman
authors.initials: K
authors.lastname: Narasimhan
authors.forename: Mahesh
authors.initials: M
authors.lastname: Choolani
authors.forename: Vladimir B
authors.initials: VB
authors.lastname: Bajic
mesh_terms: D001665:Binding Sites / Q000235:genetics; D014408:Biomarkers, Tumor / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D004967:Estrogens / Q000494:pharmacology*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects* / Q000502:physiology; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D010051:Ovarian Neoplasms / Q000175:diagnosis*; D011401:Promoter Regions, Genetic / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor; D004967:Estrogens; D014157:Transcription Factors