title: Meta-analysis of Cancer Gene Profiling Data.
abstract: The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.
authors.affiliation: Biotechnology Center, Technische Universität Dresden, Dresden, Germany.
authors.forename: Janine
authors.initials: J
authors.lastname: Roy
authors.affiliation: Faculty of Medicine, Department of Clinical Sciences, Oncology MV, University of Lund, Lund, Sweden.
authors.forename: Christof
authors.initials: C
authors.lastname: Winter
authors.affiliation: Biotechnology Center, Technische Universität Dresden, Dresden, Germany. michael.schroeder@biotec.tu-dresden.de.
authors.forename: Michael
authors.initials: M
authors.lastname: Schroeder
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D009857:Oncogenes; D011379:Prognosis; D025941:Protein Interaction Mapping / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor
keywords: Cancer biomarker; Gene expression; Network-based; Outcome prediction; PageRank