Identification of breast cancer prognostic modules via differential module selection based on weighted gene Co-expression network analysis

Breast cancer is a complex cancer which includes many different subtypes. Identifying prognostic modules, i.e., functionally related gene networks that play crucial roles in cancer development is essential in breast cancer study. Different subtypes of breast cancer correspond to different treatment methods. The purpose of this study is to use a new method to divide breast cancer into different prognostic modules, so as to provide scientific basis for improving clinical management. The method is based on comparing similarities between modules detected from different weighted gene coexpression networks. The method was applied on genomic data of breast cancer from The Cancer Genome Atlas database and was applied to select differential modules between two groups of patients with significant differences in survival times. It was compared with a previously proposed module selection method. The result shows that our method outperforms the previously proposed one. Moreover, within the identified two differential modules, the first one is highly enriched with genes involved in hormone responds, the second one is highly related with biological process engaged in M-phase. The two modules were further validated by log-rank test in the validation dataset GSE3494. Both of the two modules show significantly different with p-values less than 0.02. The identified two modules confirmed previous findings including importance of biological networks in breast cancer involved in hormone response and M-phase. Out of the top twenty hub genes in the two modules, fifteen genes were previously shown to be prognostic markers for breast cancer.