The identification of human disease-related microRNAs (miRNAs) is important for understanding the pathogenesis of diseases, but to do this experimentally is a costly and time-consuming process. Computational prediction of disease-related miRNA candidates is a valuable complement to experimental studies. It is essential to develop an effective prediction method to provide reliable candidates for subsequent biological experiments. In this study, we constructed a miRNA functional similarity network based on calculation of the functional similarity between each pair of miRNAs.
Annotation of prostate cancer (PC) genomes provides a foundation for discoveries that can improve the understanding and treatment of the disease. Therefore, in the present study, we used the Student t-test to identify differentially expressed PC-related mRNAs and microRNAs (miRNAs). Then, we performed interrelated mapping of miRNA target genes between abnormally expressed mRNAs and miRNAs, and explored mRNA-target miRNA interrelated pairs to explain the biological functions of miRNA during the progression of PC, thus revealing the occurrence of miRNA-mediated PC.