Core module biomarker identification with network exploration for breast cancer metastasis

BMC Bioinformatics

Research Article

Bernie J Daigle¹  Ruoting Yang²  Francis J Doyle³  Linda R Petzold⁴ 

[1] Department of Computer Science, University of California Santa Barbara, 93106-5110, Santa Barbara, CA, USA;Institute for Collaborative Biotechnologies, University of California Santa Barbara, 93106-5080, Santa Barbara, CA, USA;Institute for Collaborative Biotechnologies, University of California Santa Barbara, 93106-5080, Santa Barbara, CA, USA;Department of Chemical Engineering, University of California Santa Barbara, 93106-5080, Santa Barbara, CA, USA;Institute for Collaborative Biotechnologies, University of California Santa Barbara, 93106-5080, Santa Barbara, CA, USA;Department of Computer Science, University of California Santa Barbara, 93106-5110, Santa Barbara, CA, USA;Department of Mechanical Engineering, University of California Santa Barbara, 93106-5070, Santa Barbara, CA, USA;

关键词: Breast Cancer;    Core Module;    Driver Gene;    Breast Cancer Dataset;    Recursive Feature Elimination;   

DOI  :  10.1186/1471-2105-13-12

 received in 2011-09-16, accepted in 2012-01-18,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundIn a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".ResultsWe have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.ConclusionsCOMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.

【 授权许可】

Unknown   
© Yang et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.