The aim of this speech is to introduce an integrated systems biology infrastructure for cancer genes screen and annotation based on dry-lab approach. In specific, we establish a multi-functional bioinformatics platform for effectively finding gene-pair relations, clustering gene expression profiles, and mining target gene modules from vast amount of information available in both microarray data and biomedical text for cancer study. This infrastructure allows us to rapidly discover biological functions of target genes in the prostate cancer related research, and the identified candidate genes can be further studied in terms of biological functions and molecular mechanisms by wet lab experiments to confirm the prediction from in silico approach. We also present a framework for discovering and visualizing functional modules from protein-protein interactions through network clustering and Gene Ontology classifications. The integrated system implements the proposed framework such that medical researchers can gain valuable insights into the inter-relations of their genes of interests through auto-generated interactive cellular diagrams for module groups along with the correlation matrices to associated pathways and annotations.