Similarly the Leu876 mutation allows for the antagonist-agonist switch, by accommodating of enzalutamide to the ligand binding pocket. Although only LBD structural data is available for the AR-F876L mutant, we don��t believe that enzalutamide vs. androgen binding would significantly alter helices movement to drastically affect overall global AR structure, as has been observed with other AR LBD mutants. From each of the ligand mutant AR protein interaction network, we identified specific sub-network modules. These subnetworks suggest hormone-specific activated pathways involved in either tumor initiation or progression. The characterization of ontological functions across the Simetryn stimulation conditions is important as these differences or similarities in interacting proteins within the T877A-AR mutant complex may account for unique or shared cellular properties contributing to disease progression and outcomes. Therefore, we annotated significant LOUREIRIN-B GO-terms directly on sub-network modules extracted from the eight different hormone-protein interaction networks to highlight functions that may be unique to each of stimulation condition, and extracted the list of genes corresponding to those GO-terms. Using the lists of genes from the annotated GO-terms, we determined whether these gene sets are enriched in the publicly available clinical prostatic tumor microarray dataset, by applying Gene Set Enrichment Analysis. We used the clinical data set GSE21034, containing 247 clinical specimens. Initial analysis of all primary tumors from this data-set did not yield obvious enrichment of any gene-sets. However, after further inspection of the data, unique features were noted in certain tumor samples. Thus, upon returning to the patient pathology information that accompanied the clinical data-set, two diverse patient populations could be immediately discerned, and thus we resegregated our data-sets between 142 White and 25 African-American samples. By segregating the dataset along available ethnic demographical information, we immediately were able to distinctly differentiate gene-sets between White vs. African-American populations.