Lu, X.; Li, X.; Liu, P.; Qian, X.; Miao, Q.; Peng, S. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes. Molecules2018, 23, 183.
Lu, X.; Li, X.; Liu, P.; Qian, X.; Miao, Q.; Peng, S. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes. Molecules 2018, 23, 183.
Lu, X.; Li, X.; Liu, P.; Qian, X.; Miao, Q.; Peng, S. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes. Molecules2018, 23, 183.
Lu, X.; Li, X.; Liu, P.; Qian, X.; Miao, Q.; Peng, S. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes. Molecules 2018, 23, 183.
Abstract
With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.
Keywords
integrative analysis; module network; cancer subtypes; breast cancer; copy number variation; gene expression
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
