Network-based classification of breast cancer metastasis.
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.
Chuang
Han-Yu
HY
Bioinformatics Program, University of California San Diego, La Jolla, CA 92093, USA.
Lee
Eunjung
E
Liu
Yu-Tsueng
YT
Lee
Doheon
D
Ideker
Trey
T
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
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins