Motivation: Information processing tools and bioinformatics software have significantly advanced researchers ability to process and analyze biological data.Molecular data from human and model organism genomes, helps researchers identify topics for study, which in turn improves predictive accuracy, facilitates the identification of relevant genes, and simplifies the validation of laboratory data.The objective of this study was to explore the regulatory network constituted by long non-coding RNA (lncRNA), miRNA, and mRNA in prostate cancer.Results: Our study identified 57 differentially expressed miRNAs and 1252 differentially expressed mRNAs; of these, 691 were down-regulated genes primarily involved in focal adhesion, vascular smooth muscle contraction, calcium signaling pathway, and so on.The remaining 561 were up-regulated genes principally involved in systemic lupus erythematosus, progesterone-mediated oocyte maturation, oocyte meiosis, and so on.Through the integrated analysis of correlation and target gene prediction, our studies identified 1214 miRNA:mRNA pairs, including 52 miRNAs and 395 mRNAs, and screened out 455 lncRNA-miRNA pairs containing 52 miRNAs.Therefore, due to the interrelationship of lncRNAs and miRNAs with mRNAs, our study screened out 19,075 regulatory relations.