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 help 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 noncoding RNA (lncRNA), miRNA, and mRNA in prostate cancer (PCa). Microarray data of PCa were downloaded from The Cancer Genome Atlas database and DESeq package in R language were used to identify the differentially expressed genes (DEGs) between PCa and normal samples. Gene ontology enrichment analysis of DEGs was conducted using the Database for Annotation, Visualization, and Integrated Discovery. TargetScan, microcosm, miRanda, miRDB, and PicTar were used to predict target genes. LncRNA associated with PCa was exploited in the lncRNASNP database, and the LncRNA-miRNA-mRNA regulatory network was visualized using Cytoscape. Our study identified 57 differentially expressed miRNAs and 1252 differentially expressed mRNAs; of these, 691 were downregulated genes primarily involved in focal adhesion, vascular smooth muscle contraction, calcium signaling pathway, and so on. The remaining 561 were upregulated 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, owing to the interrelationship of lncRNAs and miRNAs with mRNAs, our study screened out 19,075 regulatory relationships. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways that may be involved in the pathogenesis of PCa.
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