Structural Comparison of Gene Relevance Networks for Breast Cancer Tissues in Different Grades

Author(s): Yulin Zhang, Yulin Dong, Kebo Lv, Qingfeng Zhao, Jionglong Su

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 19 , Issue 9 , 2016

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Background: The breast is an important biological system of human with two distinct states, i.e. normal and tumoral. Research on breast cancer could be based on systematic modeling to contrast the system structures of these two states.

Objective: We use mutual information for the construction of the gene network of breast tissues and normal tissues. These gene networks are analyzed, compared as well as classified. We also identify structural key genes that may play significant roles in the formation of breast cancer.

Method: Gene networks are constructed using with mutual information values. Four structural parameters, namely node degree, clustering coefficient, shortest path length and standard betweenness centrality, are used for analyzing the gene networks. Support vector machine is used to classify the gene networks into normal and disease states. Genes with standard betweenness centrality of greater than 0.3 are identified as possibly significant in the development of breast cancer.

Result: The classification of the gene networks into normal and disease states suggest that the vectors of parameters are linearly separable by any combinations of these four structural parameters. In addition, the six genes BAK1, RRAD, LCN2, EGFR, ZAP70 and FOSB are identified to possibly play significant roles in the formation of breast cancer.

Conclusion: In this work, four structural parameters have been generalized to the relevance networks. These parameters are found to distinguish gene networks of normal and cancerous breast tissues at different thresholds. In addition, the six genes identified may motivate further studies and research in breast cancer.

Keywords: Systems biology, mutual information, structural parameter, SVM, breast cancer.

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Article Details

Year: 2016
Page: [714 - 719]
Pages: 6
DOI: 10.2174/1386207319666160831152801
Price: $65

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