title: General approach to identifying potential targets for cancer imaging by integrated bioinformatics analysis of publicly available genomic profiles.
abstract: Molecular imaging has moved to the forefront of drug development and biomedical research. The identification of appropriate imaging targets has become the touchstone for the accurate diagnosis and prognosis of human cancer. Particularly, cell surface- or membrane-bound proteins are attractive imaging targets for their aberrant expression, easily accessible location, and unique biochemical functions in tumor cells. Previously, we published a literature mining of potential targets for our in-house enzyme-mediated cancer imaging and therapy technology. Here we present a simple and integrated bioinformatics analysis approach that assembles a public cancer microarray database with a pathway knowledge base for ascertaining and prioritizing upregulated genes encoding cell surface- or membrane-bound proteins, which could serve imaging targets. As examples, we obtained lists of potential hits for six common and lethal human tumors in the prostate, breast, lung, colon, ovary, and pancreas. As control tests, a number of well-known cancer imaging targets were detected and confirmed by our study. Further, by consulting gene-disease and protein-disease databases, we suggest a number of significantly upregulated genes as promising imaging targets, including cell surface-associated mucin-1, prostate-specific membrane antigen, hepsin, urokinase plasminogen activator receptor, and folate receptors. By integrating pathway analysis, we are able to organize and map "focused" interaction networks derived from significantly dysregulated entity pairs to reflect important cellular functions in disease processes. We provide herein an example of identifying a tumor cell growth and proliferation subnetwork for prostate cancer. This systematic mining approach can be broadly applied to identify imaging or therapeutic targets for other human diseases.
authors: Department of Radiology, Harvard Medical School, Boston, MA 02115, USA.
authors: Yongliang
authors: Y
authors: Yang
authors: S James
authors: SJ
authors: Adelstein
authors: Amin I
authors: AI
authors: Kassis
mesh_terms: D022126:Access to Information; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D003952:Diagnostic Imaging / Q000379:methods*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks / Q000235:genetics; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D011485:Protein Binding
chemical_list: D009363:Neoplasm Proteins