Network-based classification of breast cancer metastasis

By Han-Yu Chuang, Eunjung Lee, Yu-Tsueng Liu, Doheon Lee, Trey Ideker. 2007

1,469 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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In Molecular Biology

Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, methodology): “[11], which scores subnetworks using the mutual information between aggregated gene Z-scores and class labels.”
A human functional protein interaction network and its application to cancer data analysis (2010, background): “Protein or gene networks based on these pairwise relationships have been widely used in cancer and other disease data analysis with promising results [37-42].”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, background): “Other studies have used protein-protein interaction (PPI) networks for the same purpose [95].”
A travel guide to Cytoscape plugins (2012, background): “Finally, the PinnacleZ plugin 49 identifies subnetworks for which the average expression level is diagnostic for clinical cases versus controls.”
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Efficient Methods for Overlapping Group Lasso (2011, methodology): “[6] and employ the following two approaches for generating the overlapping gene sets (groups): pathways [33] and edges [34].”
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SLEP: Sparse Learning with Efficient Projections (2011, neutral): “, pathways [66] and edges [10].”
Inferring Pathway Activity toward Precise Disease Classification (2008, background): “expression profiles and phenotypic information will lead to more effective molecular characterization of human disease [17].”
Protein Networks as Logic Functions in Development and Cancer (2011, background): “homogenous and follows a single general function, such as the sum of gene expression levels in a module [20,25] or the difference”