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The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes

With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics d...

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Detalles Bibliográficos
Autores principales: Lu, Xinguo, Li, Xing, Liu, Ping, Qian, Xin, Miao, Qiumai, Peng, Shaoliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099653/
https://www.ncbi.nlm.nih.gov/pubmed/29364829
http://dx.doi.org/10.3390/molecules23020183
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author Lu, Xinguo
Li, Xing
Liu, Ping
Qian, Xin
Miao, Qiumai
Peng, Shaoliang
author_facet Lu, Xinguo
Li, Xing
Liu, Ping
Qian, Xin
Miao, Qiumai
Peng, Shaoliang
author_sort Lu, Xinguo
collection PubMed
description With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant 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 profiles 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.
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spelling pubmed-60996532018-11-13 The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes Lu, Xinguo Li, Xing Liu, Ping Qian, Xin Miao, Qiumai Peng, Shaoliang Molecules Article With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant 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 profiles 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. MDPI 2018-01-24 /pmc/articles/PMC6099653/ /pubmed/29364829 http://dx.doi.org/10.3390/molecules23020183 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lu, Xinguo
Li, Xing
Liu, Ping
Qian, Xin
Miao, Qiumai
Peng, Shaoliang
The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title_full The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title_fullStr The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title_full_unstemmed The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title_short The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes
title_sort integrative method based on the module-network for identifying driver genes in cancer subtypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099653/
https://www.ncbi.nlm.nih.gov/pubmed/29364829
http://dx.doi.org/10.3390/molecules23020183
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