The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to hormone refractory prostate cancer that is difficult to treat and needs newer interventions that are more effective. The objective of this study was to determine functionally-relevant biological networks, to appreciate the potential crosstalk between signaling members, and to identify biomarker signatures in prostate cancer. We used microarray analyses to identify key genes that were upregulated or down regulated at least five-fold in human prostate cancer and constructed canonical interaction networks that are important in prostate cancer through metabolomics analyses. Our prostate cancer network architecture revealed several key biomarkers including ERK1/2, JNK, p38, MEK, PI3 K, NFκB, AP-1, 14-3-3, VEGF, PDGF, Rb, WNT8 A, WNT10 A, CD44, ESR2, FSH and LH. Furthermore, the top ten transcription factors identified by TFBS-association signature analysis in the regulatory elements of co-regulated biomarkers were delineated, which may crosstalk with upstream or downstream genes elicited in our network architecture. Taken together, our results demonstrate that the regulatory interaction networks in prostate cancer provide a universal view of crosstalk between important biomarkers, i.e., key players in the pathogenesis of this disease. This will facilitate more rapid screening of functional biomarkers in early/intermediate drug discovery. The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to hormone refractory prostate cancer that is difficult to treat and needs newer interventions that are more effective. The objective of this study was to determine functionally-relevant biological networks, to appreciate the potential crosstalk between signaling members, and to identify biomarker signatures in prostate cancer. We used microarray analyzes to identify key genes that were upregulated or down regulated at least five-fold in human prostate cancer and constructed canonical interaction networks that are important in prostate cancer through metabolomics analyzes. Our prostate cancer network architecture revealed several key biomarkers including ERK1 / 2, JNK, p38, MEK, PI3 K, NFKB , AP-1, 14-3-3, VEGF, PDGF, Rb, WNT8 A, WNT10 A, CD44, ESR2, FSH and LH. top ten transcription factors identified by TFBS-association signature analysis in the regulatory elements of co-regulated biomarkers were delineated, which may crosstalk with upstream or downstream genes elicited in our network architecture. Taken together, our results demonstrates that the regulatory interaction networks in prostate cancer provide a universal view of crosstalk between important biomarkers, ie, key players in the pathogenesis of this disease. This will facilitate more rapid screening of functional biomarkers in early / intermediate drug discovery.