Next Article in Journal
Genome-Wide Identification and Comparative Analysis of the 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase (HMGR) Gene Family in Gossypium
Next Article in Special Issue
Reconstructing Phylogeny by Aligning Multiple Metabolic Pathways Using Functional Module Mapping
Previous Article in Journal
Antioxidant and Cytoprotective Effects of Tibetan Tea and Its Phenolic Components
Previous Article in Special Issue
Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Molecules 2018, 23(2), 183; https://doi.org/10.3390/molecules23020183

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

1
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
2
Hunan Want Want Hospital, Changsha 410006, China
3
School of Computer Science, National University of Defense Technology, Changsha 410073, China
*
Authors to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 29 December 2017 / Accepted: 8 January 2018 / Published: 24 January 2018
View Full-Text   |   Download PDF [704 KB, uploaded 24 January 2018]   |  

Abstract

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. View Full-Text
Keywords: integrative analysis; module network; cancer subtypes; breast cancer; copy number variation; gene expression integrative analysis; module network; cancer subtypes; breast cancer; copy number variation; gene expression
Figures

Figure 1

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. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top