AUTHOR=Wu Yu-Peng , Lin Xiao-Dan , Chen Shao-Hao , Ke Zhi-Bin , Lin Fei , Chen Dong-Ning , Xue Xue-Yi , Wei Yong , Zheng Qing-Shui , Wen Yao-An , Xu Ning 

TITLE=Identification of Prostate Cancer-Related Circular RNA Through Bioinformatics Analysis

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00892

DOI=10.3389/fgene.2020.00892

ISSN=1664-8021

ABSTRACT=Background Prostate cancer (PCa) is one of the common malignant tumors in the world. Accumulated evidence has proved that circular RNAs (circRNAs) may involve in the development and progression of various cancers and has shown great potential as novel biomarkers. Nevertheless, the in-depth mechanism and specific functions of most circRNAs in PCa remain unknown. In this study, we aimed to identify the potential circRNAs from the expression profile of prostate cancer.
Methods We utilized data downloaded from Gene Expression Omnibus (GEO) to identify critical circRNAs between PCa samples and adjacent nontumor samples. The divergent primer and the divergent primer spans back splicing junction were designed. The specificity of these designed primers was tested. The microRNA response element was predicted by the CircInteractome database. The targeted genes of microRNAs were predicted by miRDB, miRTarBase, and TaragetScan databases. Gene ontology (GO) enrichment analysis and pathway analysis showed the potential biology function of miRNA target genes. The network of circRNA-microRNA-mRNA interaction was constructed by Cytoscape. Finally, the mRNA expression of these genes was validated by TCGA and GEPIA databases. The protein expression of these genes was further validated by HPA database.  
Results A total of 60 differentially expressed circRNAs were screened and 15 circRNAs were annotated for the following analyses. GO and KEGG analyses found that these targeted genes were mainly enriched in metabolic-related analyses and PI3K-Akt signaling pathway, HIF-1 signaling pathway, p53 signaling pathway, and cell cycle pathways, etc. mRNA expression data from TCGA database were used to validate the microRNA targeted genes. A total of 11 genes were identified. The expression of mRNA and protein was further validated by GEPIA and HPA databases, respectively. We found that PDE7B, DMRT2, and TGFBR3 might be novel genes in PCa progression. Finally, three circRNA-microRNA-mRNA interaction axis were predicted by the results of bioinformatics, hsa_circ_0024353- has-miR-940- PDE7B axis, and hsa_circ_0024353-has-miR-1253-DMRT2 axis, and hsa_circ_0085494-has-miR-330-3p-TGFBR3 axis.
Conclusions To conclude, this study identified three relatively clear circRNA-microRNA-mRNA interaction axis, including hsa_circ_0024353- has-miR-940- PDE7B axis, hsa_circ_0024353-has-miR-1253-DMRT2 axis, and hsa_circ_0085494-has-miR-330-3p-TGFBR3 axis, which might contribute to novel insights into potential mechanisms of PCa.