Analyzing the LncRNA, miRNA, and mRNA Regulatory Network in Prostate Cancer with Bioinformatics Software

Abstract

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.

MicroRNA‐188 regulates aging‐associated metabolic phenotype

“…To further investigate the underlying mechanism by which miR‐188 regulates the aging‐associated metabolic phenotype, we used online tools, including TargetScan 6.2 (http://www.targetscan.org/) and miRanda (http://www.microrna.org/microrna/), to predict the potential targets of miR‐188 (He, Han, et al, ; Savita & Karunagaran, ). Among the predicted target genes, our attention was drawn to Prdm16 , because sequence analysis revealed there was a conserved miR‐188 binding site in its 3' UTR (Figure a), meanwhile PRDM16 has been reported to function as a key transcription factor that regulates the expression of thermogenic program genes in brown and beige adipocytes (Cohen et al, ; Harms et al, ).…”

LncRNA MEG3 inhibits the progression of prostate cancer by modulating miR‐9‐5p/QKI‐5 axis

“…Mounting evidence suggested that lncRNAs may exert functions by targeting miRNAs . Bian et al confirmed that UCA1 worked as a ceRNA in colorectal cancer, regulated the expression of miR‐204‐5p and induced the resistance to 5‐FU in CRC .…”

The long non-coding RNA MEG3 plays critical roles in the pathogenesis of cholesterol gallstone

“…The r > 0.95 and p-value < 0.05 were utilized for screening the signi cant results. Using miRanda database 25 (http://www.microrna.org), the miRNAs targeting the DE-lncRNAs and the DE-mRNAs were predicted.…”