Identification and Analysis of Disease Target Network of Human MicroRNA and Predicting Promising Leads for ZNF439, a Potential Target for Breast Cancer

It has become increasingly evident that manydiseases are linked to microRNAs and the role thesebiomolecules play in the development of numerous cancers.Thus, Biological network analysis offers novel approach inunderstanding basic mechanisms controlling normal biologicalprocesses and disease pathologies. Computational study of thedisease-associated networks of human microRNAs is one of themeans that provide complementary information. In the presentstudy we predicted the disease association of all humanmicroRNA predicted targets and the most promising leads wereidentified for the most potential target in breast cancer. Thecomprehensive list of human microRNAs was downloaded andthe target genes of individual microRNA were retrieved basedon miTG score using target prediction program. The resultantnetwork was investigated based on the association of nodes witha relevant pathway, disease and pathological event. Further allthe targets in the network for subjected for phylogeneticanalysis and a comparative study was performed for thesetargets. The pattern of the conserved region was derived byProsite analysis. Structure-based virtual screening involvesdocking of screened compounds and the protein target and theleads were identified based on the affinity and free energy ofbinding values. After extensive research, breast cancer wasidentified as the most significant disease and ZNF439 wasidentified as the most promising drug target in the network thatwas under study and the compounds such as Indolocarbozole,Camptothecin, Lucidenic Acid, Quercetin and Staurosporinewere predicted as promising leads for ZNF439.