Identification of Prostate Cancer-Related Circular RNA Through Bioinformatics Analysis.

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. Accumulating evidence has suggested that circular RNAs (circRNAs) are involved in the development and progression of various cancers, and they show great potential as novel biomarkers. However, the underlying mechanisms and specific functions of most circRNAs in PCa remain unknown. Here, we aimed to identify circRNAs with potential roles in PCa from the PCa expression profile. We used data downloaded from the Gene Expression Omnibus to identify circRNAs that were differentially expressed between PCa samples and adjacent non-tumor samples. Relative expression levels of identified circRNAs were validated by quantitative real-time PCR. Micro (mi)RNA response elements were predicted by the CircInteractome database, and miRNA target genes were predicted by miRDB, miRTarBase, and TargetScan databases. Gene ontology (GO) enrichment analysis and pathway analysis revealed the potential biological and functional roles of these target genes. A circRNA-miRNA-mRNA interaction network was constructed by Cytoscape. The interaction between circRNAs and miRNAs in PCa was thoroughly reviewed in the PubMed. Finally, the mRNA expression of these genes was validated by the Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The expression of proteins encoded by these genes was further validated by the Human protein Atlas (HPA) database. A total of 60 circRNAs that were differentially expressed between PCa and healthy samples were screened, of which 15 were annotated. Three circRNAs (hsa_circ_0024353, hsa_circ_0085494, hsa_circ_0031408) certified the criteria were studied. The results of quantitative real-time PCR demonstrated that the expression of hsa_circ_0024353 was significantly downregulated in PC-3 cells when compared with RWPE-1 cells, while the expression of hsa_circ_0031408 and hsa_circ_0085494 was significantly upregulated in PC-3 cells when compared with RWPE-1 cells. GO and Kyoto Encyclopedia of Genes and Genomes analyses found that target genes were mainly enriched in metabolic processes and pathways involving phosphoinositide 3-kinase-Akt signaling, hypoxia-inducible factor-1 signaling, p53 signaling, and the cell cycle. A total of 11 miRNA target genes showing differential expression between PCa and healthy samples were selected, and their mRNA and protein expression were validated by GEPIA and HPA databases, respectively. Of these, PDE7B, DMRT2, and TGFBR3 were identified as potentially playing a role in PCa progression. Finally, three circRNA-miRNA-mRNA interaction axes were predicted by bioinformatics: hsa_circ_0024353-hsa-miR-940-PDE7B, hsa_circ_0024353-hsa-miR-1253-DMRT2, and hsa_circ_0085494-hsa-miR-330-3p-TGFBR3. This study identified three circRNA-miRNA-mRNA interaction axes that might provide novel insights into the potential mechanisms underlying PCa development.

Wu YP ，Lin XD ，Chen SH ，Ke ZB ，Lin F ，Chen DN ，Xue XY ，Wei Y ，Zheng QS ，Wen YA ，Xu N ... -  《Frontiers in Genetics》

Expression profile and bioinformatics analysis of circular RNAs in acute ischemic stroke in a South Chinese Han population.

Li S ，Chen L ，Xu C ，Qu X ，Qin Z ，Gao J ，Li J ，Liu J ... -  《Scientific Reports》

Construction of circRNA-miRNA-mRNA network and identification of novel potential biomarkers for non-small cell lung cancer.

The underlying circular RNAs (circRNAs)-related competitive endogenous RNA (ceRNA) mechanisms of pathogenesis and prognosis in non-small cell lung cancer (NSCLC) remain unclear. Differentially expressed circRNAs (DECs) in two Gene Expression Omnibus datasets (GSE101684 and GSE112214) were identified by utilizing R package (Limma). Circinteractome and StarBase databases were used to predict circRNA associated-miRNAs and mRNAs, respectively. Then, protein-protein interaction (PPI) network of hub genes and ceRNA network were constructed by STRING and Cytoscape. Also, analyses of functional enrichment, genomic mutation and diagnostic ROC were performed. TIMER database was used to analyze the association between immune infiltration and target genes. Kaplan-Meier analysis, cox regression and the nomogram prediction model were used to evaluate the prognostic value of target genes. Finally, the expression of potential circRNAs and target genes was validated in cell lines and tissues by quantitative real-time PCR (qRT-PCR) and Human Protein Atlas (HPA) database. In this study, 15 DECs were identified between NSCLC tissues and adjacent-normal tissues in two GEO datasets. Following the qRT-PCR corroboration, 7 DECs (hsa_circ_0002017, hsa_circ_0069244, hsa_circ_026337, hsa_circ_0002346, hsa_circ_0007386, hsa_circ_0008234, hsa_circ_0006857) were dramatically downregulated in A549 and SK-MES-1 compared with HFL-1 cells. Then, 12 circRNA-sponged miRNAs were screened by Circinteractome and StarBase, especially, hsa-miR-767-3p and hsa-miR-767-5p were significantly up-regulated and relevant to the prognosis. Utilizing the miRDB and Cytoscape, 12 miRNA-target genes were found. Functional enrichment, genomic mutation and diagnostic analyses were also performed. Among them, FNBP1, AKT3, HERC1, COL4A1, TOLLIP, ARRB1, FZD4 and PIK3R1 were related to the immune infiltration via TIMER database. The expression of ARRB1, FNBP1, FZD4, and HERC1 was correlated with poor overall survival (OS) in NSCLC patients by cox regression and nomogram. Furthermore, the hub-mRNAs were validated in cell lines and tissues. We constructed the circRNA-miRNA-mRNA network that might provide novel insights into the pathogenesis of NSCLC and reveal promising immune infiltration and prognostic biomarkers.

Yang J ，Hao R ，Zhang Y ，Deng H ，Teng W ，Wang Z ... -  《Cancer Cell International》

Bioinformatics Analysis Predicts hsa_circ_0026337/miR-197-3p as a Potential Oncogenic ceRNA Network for Non-Small Cell Lung Cancers.

Circular RNAs (circRNAs) play an essential role in developing tumors, but their role in Non- Small Cell Lung Cancer (NSCLC) is unclear. Thus, the present study explored the possible molecular mechanism of circRNAs in NSCLC. Three circular RNA (circRNA) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expressions of circRNAs (DECs) were identified in NSCLC tissue and compared to adjacent healthy tissue. The online cancer-specific circRNA database (CSCD) was used for the analysis of the DECs function. Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Cytoscape and UALCAN were used to predict the critical nodes and perform patient survival analysis, respectively. The interaction between the DECs, the predicted miRNAs, and hub genes was also determined. Finally, the circRNA-miRNA-mRNA network was established. The expression of hsa_circ_0049271, hsa_circ_0026337, hsa_circ_0043256, and hsa_circ_0008234 was decreased in NSCLC tissues. The Encyclopedia of RNA Interactomes (ENCORI) and CSCD database results showed that hsa_circ_0026337 was found to sponge with miR-1193, miR-197-3p, miR-3605-5p, miR-433-3p and miR-652-3p, and hsa_circ_0043256 to sponge with miR-1252-5p, miR-494-3p and miR-558, respectively. Subsequently, 100 mRNAs were predicted to bind with these seven miRNA response elements (MREs). The GO analysis and KEGG pathway revealed that these 100 MREs might be involved in "histone deacetylase binding" and "cellular senescence." PPI network and Cytoscape identified the top ten hub genes. Survival analysis data showed that the low expression of hsa_circ_0026337 was significantly associated with shortened survival time in NSCLC (P = 0.037), which increased the expression level of hsa-miR-197-3p, thereby inhibiting the translation of specific proteins. This study examined the circRNA-miRNA-mRNA regulatory network associated with NSCLC and explored the potential functions of DECs in the network to elucidate the mechanisms underlying disease progression in NSCLC.

Zhang Q ，Kang L ，Li X ，Li Z ，Wen S ，Fu X ... -  《-》

Construction of endogenous RNA regulatory network for colorectal cancer based on bioinformatics.

Li Y ，Yuan F ，Lin Z ，Pan Y ... -  《-》