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Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the function of circRNAs in osteosarcoma.

ABSTRACT: Background:Osteosarcoma (OS) is a common primary malignant bone tumour. Growing evidence suggests that circular RNAs (circRNAs) are closely related to the development of tumours. However, the function of circRNAs in OS remains unknown. Here, we aimed to determine the regulatory mechanisms of circRNAs in OS. Methods:The expression profiles of OS circRNA (GSE96964), microRNA (GSE65071) and mRNA (GSE33382) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNAs, miRNAs and mRNAs in OS. A ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. MRNAs with significant prognostic differences were identified by the TARGET database in the network. Functional and pathway enrichment analyses were performed, and interactions between proteins were predicted using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the possible functions of these differentially expressed circRNAs. Results:A total of 15 downregulated circRNAs, 136 upregulated miRNAs and 52 downregulated mRNAs were identified in OS. Finally, a circRNA-miRNA-mRNA network was constructed in OS based on 14 circRNAs, 24 miRNAs, and 52 mRNAs. GO and KEGG pathway analyses suggested that the mRNAs in the network may be involved in the pathogenesis and progression of OS. Four mRNAs identified by the TARGET database were significantly associated with OS survival prognosis. A circRNA-miRNA-mRNA subnetwork was constructed based on these four mRNAs. Conclusion:Our results provide a deeper understanding of the regulatory mechanisms by which circRNAs compete for endogenous RNAs in OS.

SUBMITTER: Qiu Y

PROVIDER: S-EPMC7011443 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the function of circRNAs in osteosarcoma.

Qiu Yu Y Pu Chao C Li Yanchao Y Qi Baochuang B

Cancer cell international 20200210

<h4>Background</h4>Osteosarcoma (OS) is a common primary malignant bone tumour. Growing evidence suggests that circular RNAs (circRNAs) are closely related to the development of tumours. However, the function of circRNAs in OS remains unknown. Here, we aimed to determine the regulatory mechanisms of circRNAs in OS.<h4>Methods</h4>The expression profiles of OS circRNA (GSE96964), microRNA (GSE65071) and mRNA (GSE33382) were downloaded from the Gene Expression Omnibus (GEO) database to identify di ...[more]

PMID: 32063749

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Project description:BACKGROUND:Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and its pathogenesis is associated with abnormal post-transcriptional regulation. Unbalanced competitive endogenous RNA (ceRNA) promotes tumorigenesis and progression, and greatly contributes to tumor risk classification and prognosis. However, the comprehensive analysis of the circular RNA (circRNA)-long non-coding RNA (lncRNA)-miRNA-mRNA ceRNA network in the prognosis of AML is still rarely reported. METHOD:We obtained transcriptome data of AML and normal samples from The Cancer Genome Atlas (TCGA), Genotype-tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, and identified differentially expressed (DE) mRNAs, lncRNAs, and circRNAs. Then, the targeting relationships among lncRNA-miRNA, circRNA-miRNA, and miRNA-mRNA were predicted, and the survival related hub mRNAs were further screened by univariate and multivariate Cox proportional hazard regression. Finally, the AML prognostic circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established. RESULTS:We identified prognostic 6 hub mRNAs (TM6SF1, ZMAT1, MANSC1, PYCARD, SLC38A1, and LRRC4) through Cox regression model, and divided the AML samples into high and low risk groups according to the risk score obtained by multivariate Cox regression. Survival analysis verified that the survival rate of the high-risk group was significantly reduced (p < 0.0001). The prognostic ceRNA network of 6 circRNAs, 32 lncRNAs, 8 miRNAs, and 6 mRNAs was established according to the targeting relationship between 6 hub mRNAs and other RNAs. CONCLUSION:In this study, ceRNA network jointly participated by circRNAs and lncRNAs was established for the first time. It comprehensively elucidated the post-transcriptional regulatory mechanism of AML, and identified novel AML prognostic biomarkers, which has important guiding significance for the clinical diagnosis, treatment, and further scientific research of AML.

Project description:IntroductionCircRNA is closely correlated with a wide variety of processes of acute myeloid leukemia (AML), whereas the novel circRNAs, their molecular mechanism and the specific function they played in AML should be explored in depth.MethodsThe microarray chip data of AML patients and normal samples in the Gene Expression Omnibus (GEO) database were selected to differentially expressed (DE) circRNA, miRNA, and mRNA genes. The miRNA gene was the intersection of the circRNA target gene predicted using CSCD and the miRNA gene screened from AML patients, while the mRNA gene was the intersection of the target gene mRNA of miRNA predicted using miRanda and miRTarBase software and the mRNA gene screened from AML patients. The hub mRNAs related to survival were further screened through Cox proportional hazard regression. CircRNA/miRNA/mRNA interaction network was constructed by using Cytoscape software.10 circRNAs and 6 miRNAs in bone marrow mononuclear cells (BMMNCs) of AML patients (n=43) and healthy controls (n=35) were determined by RT-qPCR. Correlations between them were analyzed by Pearson correlation coefficient.Results10 circRNAs, 6 miRNAs, and 33 mRNAs were identified. Subsequently, the network of circRNAs, miRNAs, and hub genes was built using Cystoscope. Four key circRNAs, seven hub genes and their regulatory pathways were identified. The result of RT-qPCRs showed that hsa_circ_0009581 and hsa_circ_0005273 were significantly upregulated in AML patients while hsa_circ_0000497 and hsa_circ_0001947 were significantly downregulated. Hsa-miR-150-5p was significantly downregulated; hsa-miR-454-3p was upregulated in AML patients. Hsa_circ_0009581 and hsa-miR-150-5p; hsa_ circ_0001947 and hsa-miR-454-3p were inversely correlated using Pearson's correlation coefficient.ConclusionThis study suggests that differentially expressed circRNAs take on a critical significance to AML development and may be the effective therapeutic targets. We suppose that hsa_circ_0009581 promotes leukemia development through hsa-miR-150-5p and hsa_circ_0001947 through hsa-miR-454-3p. hsa_circ_0001947 and hsa_circ_0009581 may provide new directions in the pathogenesis of AML.

Project description:BackgroundOsteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.MethodsThe circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.ResultsA total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.ConclusionOverall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

Project description:Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics. Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the "limma" package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The "clusterProfiler" package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells. Results: Using the "limma" package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.

Dataset Information

Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the function of circRNAs in osteosarcoma.

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Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the function of circRNAs in osteosarcoma.

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