A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis

BMC Genomics

Research

Md Jamiul Jahid¹  Jianhua Ruan² 

[1] Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, 78249, San Antonio, TX, USA;Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, 78249, San Antonio, TX, USA;Cancer Therapy and Research Center, The University of Texas Health Science Center, 78229, San Antonio, TX, USA;

关键词: Breast Cancer;    Differentially Express;    Steiner Tree;    Differentially Express Gene;    Breast Cancer Metastasis;   

DOI  :  10.1186/1471-2164-13-S6-S8

来源: Springer

【 摘 要 】

BackgroundMetastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer.MethodsIn this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers.ResultsWe applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis.ConclusionsThe randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.

【 授权许可】

CC BY   
© Jahid and Ruan; licensee BioMed Central Ltd. 2012