Identification of breast cancer patients based on human signaling network motifs

Identifying breast cancer patients is crucial to the clinical diagnosis and therapy for this disease. Conventional gene-based methods for breast cancer diagnosis ignore gene-gene interactions and thus may lead to loss of power. In this study, we proposed a novel method to select classification features, called "Selection of Significant Expression-Correlation Differential Motifs" (SSECDM). This method applied a network motif-based approach, combining a human signaling network and high-throughput gene expression data to distinguish breast cancer samples from normal samples. Our method has higher classification performance and better classification accuracy stability than the mutual information (MI) method or the individual gene sets method. It may become a useful tool for identifying and treating patients with breast cancer and other cancers, thus contributing to clinical diagnosis and therapy for these diseases.