Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783348715234263040 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6099653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT luxinguo theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT lixing theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT liuping theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT qianxin theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT miaoqiumai theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT pengshaoliang theintegrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT luxinguo integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT lixing integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT liuping integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT qianxin integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT miaoqiumai integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes AT pengshaoliang integrativemethodbasedonthemodulenetworkforidentifyingdrivergenesincancersubtypes |