Purpose\nProstate cancer is one of the leading malignancies affecting human male population worldwide. In this study, we identified differentially expressed genes, microRNAs (miRNAs) using large number of publically available heterogeneous datasets generated using high-throughput sequencing (NGS) technologies from prostate cancer and normal tissues. Targets of selected miRNAs were predicted using state of the art target prediction methods.\n\nMethods\nDifferential expression analysis was performed on The Cancer Genome Atlas (TCGA) Level 3 gene and miRNA expression datasets to identify differentially expressed, up and down-regulated genes and miRNAs in prostate cancer. Later inversely expressing target DEGs of 10 highly up and down-regulated miRNAs (DEMirs) were predicted using target prediction methods. Regulatory networks summarizing the regulatory links among the DEMirs and DEGs were created using Cytoscape.\n\nResults\nExpression analysis results led to identification of 844 up-regulated and 1123 down-regulated genes in prostate cancer while 101 up and, 30 down-regulated miRNAs in prostate cancerous tissues as compared to normal cells. These highly up regulated gene and, miRNAs can act as non-invasive biomarkers for prostate cancer detection. Gene Ontology (GO) and KEGG pathway enrichment analysis showed highly up-regulated genes in prostate cancer were involved in biological process associated with cell division. Highly up-regulated miRNAs in prostate cancer miR-483-3p, miR-483-5p etc. were found to be targeting tumor, metastasis suppressor genes.\n\nConclusion\nWe identified a molecular expression signature and pathway regulatory mechanisms in prostate cancer with potential diagnostic and therapeutic value. These findings may extend our understanding of the molecular mechanisms underlying prostate cancer and help prevent, diagnose, treatment of the disease.