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