A pathways-based prediction model for classifying breast cancer subtypes
Metrics: PDF 1362 views | HTML 2296 views | ?
Tong Wu1, Yunfeng Wang2, Ronghui Jiang3, Xinliang Lu4 and Jiawei Tian1
1Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China
2College of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang Province, China
3Department of Surgery, Yanbian No.2 People’s Hospital, Jilin Province, China
4Institute of Immunology, Zhejiang University School of Medicine, Zhejiang Province, China
Jiawei Tian, email: email@example.com
Keywords: breast cancer, subtype-specific gene, pathway enrichment, co-expression network, classification prediction model
Received: August 17, 2016 Accepted: May 01, 2017 Published: June 17, 2017
Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis. Three support vector machine (SVM) classification models based on the identified pathways effectively classified different breast cancer subtypes, suggesting that breast cancer subtype-specific risk assessment based on disease pathways could be a potentially valuable approach. Our analysis not only provides insight into breast cancer subtype-specific mechanisms, but also may improve the accuracy of SVM classification models.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.