Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data.
10.4048/jbc.2017.20.3.240
- Author:
Hee Chan JUNG
1
;
Sung Hwan KIM
;
Jeong Hoon LEE
;
Ju Han KIM
;
Sung Won HAN
Author Information
1. Department of Internal Medicine, Eulji University College of Medicine, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Genes;
Oncogenes;
Triple negative breast neoplasms
- MeSH:
Breast Neoplasms;
Gene Expression*;
Gene Regulatory Networks*;
Genome;
Methods;
Oncogenes;
Physiology;
Triple Negative Breast Neoplasms*
- From:Journal of Breast Cancer
2017;20(3):240-245
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: To better identify the physiology of triple-negative breast neoplasm (TNBN), we analyzed the TNBN gene regulatory network using gene expression data. METHODS: We collected TNBN gene expression data from The Cancer Genome Atlas to construct a TNBN gene regulatory network using least absolute shrinkage and selection operator regression. In addition, we constructed a triple-positive breast neoplasm (TPBN) network for comparison. Furthermore, survival analysis based on gene expression levels and differentially expressed gene (DEG) analysis were carried out to support and compare the network analysis results, respectively. RESULTS: The TNBN gene regulatory network, which followed a power-law distribution, had 10,237 vertices and 17,773 edges, with an average vertex-to-vertex distance of 8.6. The genes ZDHHC20 and RAPGEF6 were identified by centrality analysis to be important vertices. However, in the DEG analysis, we could not find meaningful fold changes in ZDHHC20 and RAPGEF6 between the TPBN and TNBN gene expression data. In the multivariate survival analysis, the hazard ratio for ZDHHC20 and RAPGEF6 was 1.677 (1.192–2.357) and 1.676 (1.222–2.299), respectively. CONCLUSION: Our TNBN gene regulatory network was a scale-free one, which means that the network would be easily destroyed if the hub vertices were attacked. Thus, it is important to identify the hub vertices in the network analysis. In the TNBN gene regulatory network, ZDHHC20 and RAPGEF6 were found to be oncogenes. Further study of these genes could help to reveal a novel method for treating TNBN in the future.
