With the development of next generation sequencing technologies, more and more omics data will be accumulated for integrative analysis. We have aimed to develop novel methods to integrate omics data and to identify cancer associated gene sets, pathways and modules. In the present talk, we will present our recent findings based on the integrative analysis of prostate cancer microarray expression datasets at systems biological level. The work verified the hypothesis that the similarity of the common molecular signature at the pathway level are generally higher than that at the gene level. Based on this hypothesis, we indentified several novel prostate cancer associated pathways, especially the Endothelin-1/EDNRA transactivation of EGFR pathway which was overlapped in 9 data sets. The identified novel prostate cancer related pathways need further experimental testing and will provide essential information for the future personalized medicine study.