ArticleMolecules and cells2012
A pathway-based classification of breast cancer integrating data on differentially expressed genes, copy number variations and microRNA target genes.
Article in Molecules and cells, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches.Computational intelligence and neuroscience · 2020Article
- In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer.International journal of molecular sciences · 2018Article
- Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors.BMC bioinformatics · 2017Article
- Article
- Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential.BMC systems biology · 2015Review
- MicroRNA-10b and minichromosome maintenance complex component 5 gene as prognostic biomarkers in breast cancer.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2015Article
- Article
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Authors and funding
5 authors.
Funding
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Abstract
Breast cancer is a clinically heterogeneous disease characterized by distinct molecular aberrations. Understanding the heterogeneity and identifying subgroups of breast cancer are essential to improving diagnoses and predicting therapeutic responses. In this paper, we propose a classification scheme for breast cancer which integrates data on differentially expressed genes (DEGs), copy number variations (CNVs) and microRNAs (miRNAs)-regulated mRNAs. Pathway information based on the estimation of molecular pathway activity is also applied as a postprocessor to optimize the classifier. A total of 250 malignant breast tumors were analyzed by k-means clustering based on the patterns of the expression profiles of 215 intrinsic genes, and the classification performances were compared with existing breast cancer classifiers including the BluePrint and the 625-gene classifier. We show that a classification scheme which incorporates pathway information with various genetic variations achieves better performance than classifiers based on the expression levels of individual genes, and propose that the identified signature serves as a basic tool for identifying rational therapeutic opportunities for breast cancer patients.
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Registered trials
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