Dataset Information

Bioinformatics analysis of potential glioblastoma circular RNA sponge network.

ABSTRACT:

Background

Circular RNA is emerging functional molecule for glioblastoma. However, the function and regulatory of circular RNA (circRNA) remains unclear. In this study, the circRNA sequencing and array data of glioblastoma were analyzed by multiple bioinformatics methods to establish a potential molecular sponge mechanism regulation network.

Methods

Gene Expression Omnibus datasets were used to extract circRNAs. CircInteractome was used to predict microRNAs binding to circRNAs. Chinese Glioma Gene Atlas database was used to screen the microRNAs with expression and survival trends. MiRabel database was used to predict potential gene targets of microRNAs. The Cancer Genome Atlas database was used to screen the gene targets of sponge network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were to explain the gene targets functions. R software, Cytoscape software and Bioinformatics website were used to establish the network and visualize the results.

Results

Hsa_circ_0000219, hsa_circ_0001073 and hsa_circ_0070700 were selected from more than 2000 differentially expressed circRNAs of Gene Expression Omnibus Series (GSE) GSE146463, GSE92322 and GSE86202 datasets. Hsa-miR-1248 and hsa-miR-1290 were up regulated and related to glioblastoma poor prognosis. Targets of these microRNAs including ARHGEF7, CELA2b, RNF11, YPEL1 and ZNF37a were also screened via expression and survival data. Gene targets function were mainly enriched in signal transduction, cell plasma membrane, ATP binding and calcium signaling pathway.

Conclusions

A circRNA molecular sponge regulatory network including hsa-miR-1248 and hsa-miR-1290 has been established. In this network, hsa_circ_0001073, hsa_circ_0070700, hsa_circ_0000219, hsa-miR-1248, hsa-miR-1290, and RNF11 may have the potential being emerging glioblastoma therapeutic targets. However, their function and significance for glioblastoma need further experiments to verify.

SUBMITTER: Zhao L

PROVIDER: S-EPMC9189202 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Bioinformatics analysis of potential glioblastoma circular RNA sponge network.

Zhao Liwen L Zhang Pengfei P Nan Yang Y Ren Bingcheng B Ma Haiwen H Xie Jiapeng J Huang Qiang Q

Translational cancer research 20220501 5

<h4>Background</h4>Circular RNA is emerging functional molecule for glioblastoma. However, the function and regulatory of circular RNA (circRNA) remains unclear. In this study, the circRNA sequencing and array data of glioblastoma were analyzed by multiple bioinformatics methods to establish a potential molecular sponge mechanism regulation network.<h4>Methods</h4>Gene Expression Omnibus datasets were used to extract circRNAs. CircInteractome was used to predict microRNAs binding to circRNAs. Ch ...[more]

PMID: 35706804

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Project description:Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-associated mortality. Lung adenocarcinoma (LAC) is the most prevalent pathological subtype of NSCLC and accounts for ~40% of all lung cancer mortalities. There remains an urgent demand for the identification of novel biomarkers for the diagnosis and development of therapeutic strategies for LAC. In the present study, the profiles of the differentially-expressed circular RNAs (circRNAs) in LAC tissues compared with those in their corresponding non-cancerous tissues were obtained after analyzing the circRNA microarray dataset GSE101586. The expression pattern of the indicated circRNAs in the LAC tissues were subsequently verified using reverse transcription-quantitative PCR (RT-qPCR). The potential prognostic significance of these circRNAs in patients with LAC were then analyzed in a retrospective clinical study. A circRNA-microRNA (miR or miRNA)-mRNA regulatory network in LAC was established by using Cytoscape. In addition, a protein-protein interaction (PPI) network was plotted using the Search Tool for the Retrieval of Interacting Genes/Proteins and visualized through Cytoscape. The prognostic value of the hub genes found was then analyzed based on the Gene Expression Profiling Interactive Analysis database. In total, four differentially-expressed circRNAs were obtained from the GSE101586 microarray dataset, three of which (hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666) were confirmed by RT-qPCR to be highly expressed in LAC tissues. This retrospective clinical study revealed that higher expression levels of these three circRNAs were associated with poorer prognoses in patients with LAC. In addition, siRNA-mediated knockdown of these circRNAs was found to inhibit cell proliferation, migration and invasion in LAC cells. Following analysis of the molecular mechanism underlying these circRNAs, eight miRNAs, namely miR-520f, miR-1261, miR-1270, miR-620, miR-188-3p, miR-516b, miR-940 and miR-661, were identified with potential binding sites for these three circRNAs. Subsequently, 232 overlapped genes from the 795 upregulated genes in the LAC samples from The Cancer Genome Atlas database and 7,829 predicted target genes of the list of eight aforementioned miRNAs were obtained. A circRNA-miRNA-mRNA network was then constructed. A PPI network was established, with six hub genes, namely kinesin family member (KIF) 2C, KIF18B, maternal embryonic leucine zipper kinase, baculoviral IAP repeat-containing 5, polo-like kinase 1 and cytoskeleton-associated protein 2-like, determined from this network. Higher expression levels of each of these hub genes were found to be associated with poorer prognoses of patients with LAC. To conclude, data from the present study suggested that circRNAs hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 have the potential to be viable biomarkers and therapeutic targets for LAC.

Project description:BackgroundProstate 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.MethodsWe 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.ResultsA 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.ConclusionThis study identified three circRNA-miRNA-mRNA interaction axes that might provide novel insights into the potential mechanisms underlying PCa development.

Dataset Information

Bioinformatics analysis of potential glioblastoma circular RNA sponge network.

Background

Methods

Results

Conclusions

Publications

Bioinformatics analysis of potential glioblastoma circular RNA sponge network.

Similar Datasets

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