Dataset Information

Identification of a potentially functional circRNA-miRNA-mRNA regulatory network for investigating pathogenesis and providing possible biomarkers of bladder cancer.

ABSTRACT: Background:Circular RNAs (circRNAs) have received considerable attention in human cancer research. However, many circRNAs remain to be detected. In our study, we determined novel circRNAs and investigated their effects on bladder cancer (BCa). Methods:Microarray dataset GSE92675 was downloaded from Gene Expression Omnibus (GEO). Then, we combined computational biology with quantitative real-time polymerase chain reaction (qRT-PCR) to select related circRNAs in BCa. The selected circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory subnetwork was determined by Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Results:The regulatory network constructed from the microarray dataset (GSE92675) contained 49 differentially expressed circRNAs (DECs). GO and KEGG analyses showed that the MAPK and PI3K-AKT signaling pathways were statistically significant. On the basis of qRT-PCR and the degree value calculated by the cytoHubba plugin of Cytoscape, hsa_circ_0011385 was finally confirmed. The subnetwork around hsa_circ_0011385 was constructed. In addition, we created a protein-protein interaction (PPI) network composed of 67 nodes and 274 edges after removing independent nodes. GO and KEGG analyses showed that hubgenes were involved in cell cycle activities. Moreover, they could be regulated by miRNAs and play an eventful role in BCa pathogenesis. Conclusions:We proposed a novel circRNA-miRNA-mRNA network related to BCa pathogenesis. This network might be a new molecular biomarker and could be used to develop potential treatment strategies for BCa.

SUBMITTER: Lu HC

PROVIDER: S-EPMC6990554 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of a potentially functional circRNA-miRNA-mRNA regulatory network for investigating pathogenesis and providing possible biomarkers of bladder cancer.

Lu Hong-Cheng HC Yao Jia-Qi JQ Yang Xiao X Han Jie J Wang Jing-Zi JZ Xu Kun K Zhou Rui R Yu Hao H Lv Qiang Q Gu Min M

Cancer cell international 20200129

<h4>Background</h4>Circular RNAs (circRNAs) have received considerable attention in human cancer research. However, many circRNAs remain to be detected. In our study, we determined novel circRNAs and investigated their effects on bladder cancer (BCa).<h4>Methods</h4>Microarray dataset GSE92675 was downloaded from Gene Expression Omnibus (GEO). Then, we combined computational biology with quantitative real-time polymerase chain reaction (qRT-PCR) to select related circRNAs in BCa. The selected ci ...[more]

PMID: 32015691

Similar Datasets

Project description:BACKGROUND As all we know, gastric cancer (GC) is a highly aggressive disease. Recently, circular RNA (circRNA) was found to play a vital role in regulation of GC. Some circRNAs could regulate messenger RNA (mRNA) expression by functioning as a microRNA (miRNA) sponge. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved GC rarely has been explored and researched. MATERIAL AND METHODS All the differentially expressed circRNAs, miRNAs, and mRNA were derived from Gene Expression Omnibus (GEO) microarray data (GSE78092, GSE89143, GSE93415, and GSE54129). GC level 3 miRNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Furthermore, a circRNA-miRNA-mRNA regulatory network was constructed by Cytoscape (version 3.6.1). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed the functions and signaling pathways associated with these target genes. Hub genes of protein-protein interaction (PPI) network were identified by STRING database and cytoHubba. RESULTS The regulatory network consists of 3 circRNAs, 22 miRNAs, and 128 mRNAs. Only 3 miRNAs of the network were consistent with the expression of TCGA and were associated with some clinical features. The results of the functional analysis of 128 mRNAs showed that GO analysis and KEGG pathways of inclusion criteria were 49 and 24, respectively. PPI network and Cytoscape showed that the top 10 hub genes were MYC, CTGF, TGFBR2, TGFBR1, SERPINE1, KRAS, ZEB1, THBS1, CDK6, and TNS1; 4 of which were verified by GEPIA based on TCGA. Highly expressed SERPINE1 had a poor OS (over survival) and DFS (disease-free survival), and TGFBR1 expression increased along with the increase of clinical stages. CONCLUSIONS This study looked at a circRNA-miRNA-mRNA regulatory network associated with GC and explored the potential functions of mRNA in the network, then identified a new molecular marker for prediction, prognosis, and therapeutic targets for clinical patients.

Project description:BackgroundCircular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC).MethodsA combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC.ResultsSix differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA-miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein-protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis.ConclusionsOur study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA-miRNA-mRNA network view.

Project description:BackgroundCircular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identification and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC.MethodsA comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis.ResultsSix overlapped differentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Subsequent RT-qPCR analysis of GC samples confirmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA-miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 differently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identified. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling pathways. A protein-protein interaction network with the 320 target genes was constructed using STRING, and fifteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identified. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the fifteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis.ConclusionsThis study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.

Dataset Information

Identification of a potentially functional circRNA-miRNA-mRNA regulatory network for investigating pathogenesis and providing possible biomarkers of bladder cancer.

Publications

Identification of a potentially functional circRNA-miRNA-mRNA regulatory network for investigating pathogenesis and providing possible biomarkers of bladder cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets