J Breast Cancer.  2017 Sep;20(3):240-245. 10.4048/jbc.2017.20.3.240.

Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data

• Jung HC
• Kim SH
• Lee JH
• Kim JH
• Han SW

• KMID: 2438990
• DOI: http://doi.org/10.4048/jbc.2017.20.3.240

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.

Keyword

Genes; Oncogenes; Triple negative breast neoplasms

MeSH Terms

Breast Neoplasms
Gene Expression*
Gene Regulatory Networks*
Genome
Methods
Oncogenes
Physiology
Triple Negative Breast Neoplasms*

Figure

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  Figure 1 Cluster analysis of the triple-negative breast neoplasm gene regulatory network using the Clauset-Newman-Moore algorithm. The largest group (blue) and the second largest group (sky-blue) are connected the most frequently.

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  Figure 2 Cluster analysis of the triple-positive breast neoplasm gene regulatory network using the Clauset-Newman-Moore algorithm. The second largest group (red) and the third largest group (green) are connected the most frequently.

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  Figure 3 Regression analysis of the observed vertex degree and density values. (A) Regression analysis of degree exist in TN has slope -2.823, adjusted R2 0.882, and p<0.001 which satisfy the power-law distribution. (B) Regression analysis of degree exist in TP has slope -2.727, adjusted R2 0.897, and p<0.001 which satisfy the power-law distribution.Degree exist in TN=triple-negative breast neoplasm group; Degree exist in TP=triple-positive breast neoplasm group.

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