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Metabolomics (Los Angel). 2012 May 5;2(112). pii: 6451.

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

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

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.

KEYWORDS:

Bioinformatics; Biomedical literature data mining; Natural Language Processing (NLP); Novel cancer biomarkers; Ontology

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