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ISSN: 2153-0769

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Research Article

Bioprospecting the Bibleome: Adding Evidence to Support the Inflammatory Basis of Cancer

Peter L. Elkin1*, Andrew Frankel2, Ester H. Liebow-Liebling2, Jared R. Elkin2, Mark S. Tuttle1 and Steven H. Brown3,4

1Center for Biomedical Informatics, Mount Sinai School of Medicine, New York, NY, USA

2Mayo Clinic College of Medicine, Rochester, MN, USA

3Vanderbilt University, Nashville, TN, USA

4Department of Veterans Affairs, Nashville, TN, USA

*Corresponding Author:
Peter L. Elkin, M.D
MACP, FACMI, Mount Sinai School of Medicine
Center for Biomedical Informatics, New York, USA
Tel: 212-860- 3837
Fax: 212-824-2329
E-mail: [email protected]

Received date: July 19, 2011; Accepted date: May 03, 2012; Published date: May 05, 2012

Citation: Elkin PL, Frankel A, Liebow-Liebling EH, Elkin JR, Tuttle MS, et al. (2012) Bioprospecting the Bibleome: Adding Evidence to Support the Inflammatory Basis of Cancer. Metabolomics 2:112. doi: 10.4172/2153-0769.1000112

Copyright: © 2012 Elkin PL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background, cancer significance and question: BioProspecting is a novel approach that enabled our team to mine genetic marker related data from the New England Journal of Medicine (NEJM) utilizing Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and the Human Gene Ontology (HUGO). Genes associated with disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) Natural Language Processing (NLP)engine, whose output was represented as an ontology-network incorporating the semantic encodings of the literature. Metabolic functions were used to identify potentially novel relationships between (genes or proteins) and (diseases or drugs). In an effort to identify genes important to transformation of normal tissue into a malignancy, we went on to identify the genes linked to multiple cancers and then mapped those genes to metabolic and signaling pathways. Findings: Ten Genes were related to 30 or more cancers, 72 genes were related to 20 or more cancers and 191 genes were related to 10 or more cancers. The three pathways most often associated with the top 200 novel cancer markers were the Acute Phase Response Signaling, the Glucocorticoid Receptor Signaling and the Hepatic Fibrosis/ Hepatic Stellate Cell Activation pathway. Meaning and implications of the advance: This association highlights the role of inflammation in the induction and perhaps transformation of mortal cells into cancers. Major findings: BioProspecting can speed our identification and understanding of synergies between articles in the biomedical literature. In this case we found considerable synergy between the Oncology literature and the Sepsis literature. By mapping these associations to known metabolic, regulatory and signaling pathways we were able to identify further evidence for the inflammatory basis of cancer.

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