title: Colorectal cancer drug target prediction using ontology-based inference and network analysis.
abstract: Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.
authors.affiliation: Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Cui
authors.initials: C
authors.lastname: Tao
authors.affiliation: Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Jingchun
authors.initials: J
authors.lastname: Sun
authors.affiliation: Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: W Jim
authors.initials: WJ
authors.lastname: Zheng
authors.affiliation: Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Junjie
authors.initials: J
authors.lastname: Chen
authors.affiliation: Center for Computational Biomedicine, School of Biomedical informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA and Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA hua.xu@uth.tmc.edu.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000970:Antineoplastic Agents / Q000737:chemistry* / Q000493:pharmacokinetics / Q000494:pharmacology; D015179:Colorectal Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism; D016208:Databases, Factual; D016503:Drug Delivery Systems; D015195:Drug Design; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism
chemical_list: D000970:Antineoplastic Agents; D009363:Neoplasm Proteins
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