title: Network-based classification of breast cancer metastasis.
abstract: Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
authors.affiliation: Bioinformatics Program, University of California San Diego, La Jolla, CA 92093, USA.
authors.forename: Han-Yu
authors.initials: HY
authors.lastname: Chuang
authors.forename: Eunjung
authors.initials: E
authors.lastname: Lee
authors.forename: Yu-Tsueng
authors.initials: YT
authors.lastname: Liu
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
authors.forename: Trey
authors.initials: T
authors.lastname: Ideker
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology*; D019295:Computational Biology; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009362:Neoplasm Metastasis / Q000235:genetics / Q000473:pathology*; D009363:Neoplasm Proteins / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins