title: A combined approach to data mining of textual and structured data to identify cancer-related targets.
abstract: BACKGROUND
  We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein databases (UniProt, InterPro, NCBI Entrez), and state-of-the-art pathway knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.
  
  
  RESULTS
  The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, approximately 35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.
  
  
  CONCLUSIONS
  The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.
authors.affiliation: Harvard Medical School, Department of Radiology, 200 Longwood Avenue, Boston, Massachusetts, USA. pavel_pospisil@hms.harvard.edu
authors.forename: Pavel
authors.initials: P
authors.lastname: Pospisil
authors.forename: Lakshmanan K
authors.initials: LK
authors.lastname: Iyer
authors.forename: S James
authors.initials: SJ
authors.lastname: Adelstein
authors.forename: Amin I
authors.initials: AI
authors.lastname: Kassis
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D003628:Database Management Systems; D030562:Databases, Protein; D016503:Drug Delivery Systems / Q000145:classification / Q000379:methods; D018390:Gene Targeting / Q000145:classification / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D009363:Neoplasm Proteins / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D039781:PubMed; D018511:Systems Integration
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins