Dataset Information

Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis.

ABSTRACT: Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar's home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

SUBMITTER: Gu W

PROVIDER: S-EPMC5878900 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis.

Gu Wei W Sun Ying Y Zheng Xiong X Ma Jin J Hu Xiao-Ying XY Gao Tian T Hu Mei-Jie MJ

BioMed research international 20180318

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The mi ...[more]

PMID: 29744354

Similar Datasets

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.

Project description:Previous studies pointed out that a variety of microRNAs (miRNAs) are involved in the pathogenesis of neonatal acute respiratory distress syndrome (NARDS) and play different roles in the pathological process. However, there have been few studies reporting the connection between circular RNA (circRNA) and NARDS, so the expression profile of circRNAs in newborns with acute respiratory distress syndrome remains largely unknown. In the present study, 10 samples obtained from remaining clinical blood samples of newborns hospitalized in a neonatal ward of the First Affiliated Hospital of Nanjing Medical University from January 2020 to October 2020 were divided into the “NARDS” group and “non-NARDS” group according to the Montelux standard and then were analyzed in microarray, and 10 other samples collected from the same place and from January 1, 2021 to August 31, 2021, were used to do RT-qPCR experiment. circRNA expression profiles, in which 741 circRNAs were downregulated and 588 were upregulated, were screened with circRNA high-throughput sequencing. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of parent genes of the differentially expressed circRNAs revealed that these circRNAs may be related to the process of protein synthesis and metabolism in NARDS. Moreover, five circRNAs—hsa_circ_0058495, hsa_circ_0000367, hsa_circ_0005389, hsa_circ_0059571, and hsa_circ_0006608—were selected randomly among the top 10 circRNAs of the downregulated or upregulated expression profiles. Then, bioinformatics tools were used to predict correlative miRNA and its target genes, which were also subjected to the same bioinformatics analysis for further study. The top 30 enriched KEGG pathway analyses of the 125 target genes suggested that these target genes are widely involved in the synthesis and secretion of endocrine hormones, and the top 30 enriched GO terms based on the 125 target genes are also focused on the protein and DNA processing. Thus, the present results show that circRNAs could promote the inflammation of NARDS which may provide a new therapeutic direction and it can be used as molecular markers for early diagnosis of NARDS, but further molecular biology verification is needed to define the specific role of differentially expressed circRNAs in NARDS.

Dataset Information

Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis.

Publications

Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets