Network-based classification of breast cancer metastasis. Export

@article{citeulike:1777140, 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.}, author = {Chuang, Han-Yu Y. and Lee, Eunjung and Liu, Yu-Tsueng T. and Lee, Doheon and Ideker, Trey}, citeulike-article-id = {1777140}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/msb4100180}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/msb4100180}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17940530}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17940530}, day = {16}, doi = {10.1038/msb4100180}, issn = {1744-4292}, journal = {Molecular systems biology}, keywords = {cancer, clustering, network}, month = {October}, posted-at = {2007-10-17 01:53:38}, priority = {3}, publisher = {EMBO and Nature Publishing Group}, title = {Network-based classification of breast cancer metastasis.}, url = {http://dx.doi.org/10.1038/msb4100180}, volume = {3}, year = {2007} }

TY - JOUR ID - citeulike:1777140 L3 - citeulike-article-id:1777140 N2 - 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. JF - Molecular systems biology SN - 1744-4292 TI - Network-based classification of breast cancer metastasis. PB - EMBO and Nature Publishing Group VL - 3 KW - cancer KW - clustering KW - network AU - Chuang, Han-Yu AU - Lee, Eunjung AU - Liu, Yu-Tsueng AU - Lee, Doheon AU - Ideker, Trey PY - 2007/10/16/ UR - http://dx.doi.org/10.1038/msb4100180 ER -