Format

Send to

Choose Destination
Molecules. 2018 Jan 24;23(2). pii: E183. doi: 10.3390/molecules23020183.

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

Author information

1
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. xingleo@hnu.edu.cn.
2
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
3
Hunan Want Want Hospital, Changsha 410006, China. lp-simple123@126.com.
4
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. qianxin@hnu.edu.cn.
5
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. qiumaimiao@hnu.edu.cn.
6
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. hnluxinguo@hnu.edu.cn.
7
School of Computer Science, National University of Defense Technology, Changsha 410073, China. hnluxinguo@hnu.edu.cn.

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.

KEYWORDS:

breast cancer; cancer subtypes; copy number variation; gene expression; integrative analysis; module network

PMID:
29364829
PMCID:
PMC6099653
DOI:
10.3390/molecules23020183
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center