Abstract
Breast cancer is a highly heterogeneous disease and it is generally divided into four subtypes in clinical practice. Common differentially expressed genes are always ignored. In fact, the regulatory associations of common differentially expressed genes exhibit significant differences among the four subtypes of breast cancer. A deep differential analysis in four subtype of breast cancer is proposed in this paper. The common differentially expressed genes among four subtypes of breast cancer are mainly considered. The miRNA-mRNA regulatory network is constructed as a bipartite network and the regulations of miRNA-mRNA for each subtype of breast cancer are predicted. The common differentially expressed genes for four subtypes of breast cancer are obtained. Breast cancer is classified into four subtypes by using Prediction Analysis of Microarray 50. The method of EdgeR is employed to obtain the common differentially expressed genes. A background network is designed by the common differentially expressed genes. MiRNA-mRNA bipartite network is constructed by the background network. A method of weighted similarity information (WSI) is proposed. Global similarity information of miRNA and mRNA are obtained by the WSI, respectively. The regulations of miRNA-mRNA in four subtypes of breast cancer are predicted by integrating the MiRNA-mRNA bipartite network and the global similarity information of miRNA and mRNA. In 5-fold cross-validation, this method performs well across the four subtypes of breast cancer. In addition, the predicted regulations of miRNA-mRNA have 85% ratio in the miRWalk2.0 database. This represents a 30% improvement over traditional methods.