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      Three novel circRNAs upregulated in tissue and plasma from hepatocellular carcinoma patients and their regulatory network

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          Abstract

          Background

          The regulatory roles of circular RNAs (circRNAs) in tumorigenesis have attracted increasing attention. However, novel circRNAs with the potential to be used as serum/plasma biomarkers and their regulatory mechanism in the pathogenesis of hepatocellular carcinoma (HCC) remain explored.

          Methods

          CircRNA expression profiles of tumor tissues and plasma samples from HCC patients were compiled and jointly analyzed. CircRNA–miRNA–mRNA interactions were predicted by bioinformatics tools. The expression of interacting miRNAs and mRNA was verified in independent datasets. Survival analysis and pathway enrichment analysis were conducted on hub genes.

          Results

          We identified three significantly up-regulated circRNAs (hsa_circ_0009910, hsa_circ_0049783, and hsa_circ_0089172) both in HCC tissues and plasma samples. Two of them were validated to be indeed circular and could be excreted from hepatoma cells. We further revealed four miRNAs (hsa-miR-455-5p, hsa-miR-615-3p, hsa-miR-18a-3p, hsa-miR-4524a-3p) that targeting circRNAs and expressed in human HCC samples, and 95 mRNAs targeted by miRNAs and significantly up-regulated in two HCC cohorts. A protein-protein interaction network revealed 19 hub genes, 12 of them (MCM6, CCNB1, CDC20, NDC80, ZWINT, ASPM, CENPU, MCM3, MCM5, ECT2, CDC7, and DLGAP5) were associated with reduced survival in two HCC cohorts. KEGG, Reactome, and Wikipathway enrichment analysis indicated that the hub genes mainly functioned in DNA replication and cell cycle.

          Conclusions

          Our study uncovers three novel deregulated circRNAs in tumor and plasma from HCC patients and provides an insight into the pathogenesis from the circRNA–miRNA–mRNA regulatory network.

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          Most cited references41

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          Natural RNA circles function as efficient microRNA sponges.

          MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression that act by direct base pairing to target sites within untranslated regions of messenger RNAs. Recently, miRNA activity has been shown to be affected by the presence of miRNA sponge transcripts, the so-called competing endogenous RNA in humans and target mimicry in plants. We previously identified a highly expressed circular RNA (circRNA) in human and mouse brain. Here we show that this circRNA acts as a miR-7 sponge; we term this circular transcript ciRS-7 (circular RNA sponge for miR-7). ciRS-7 contains more than 70 selectively conserved miRNA target sites, and it is highly and widely associated with Argonaute (AGO) proteins in a miR-7-dependent manner. Although the circRNA is completely resistant to miRNA-mediated target destabilization, it strongly suppresses miR-7 activity, resulting in increased levels of miR-7 targets. In the mouse brain, we observe overlapping co-expression of ciRS-7 and miR-7, particularly in neocortical and hippocampal neurons, suggesting a high degree of endogenous interaction. We further show that the testis-specific circRNA, sex-determining region Y (Sry), serves as a miR-138 sponge, suggesting that miRNA sponge effects achieved by circRNA formation are a general phenomenon. This study serves as the first, to our knowledge, functional analysis of a naturally expressed circRNA.
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            Hepatocellular Carcinoma

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              An automated method for finding molecular complexes in large protein interaction networks

              Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from .
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                Author and article information

                Contributors
                lh_wang@shzu.edu.cn
                wxwshz@126.com
                chenxueling@shzu.edu.cn
                Journal
                Cancer Cell Int
                Cancer Cell Int
                Cancer Cell International
                BioMed Central (London )
                1475-2867
                22 January 2021
                22 January 2021
                2021
                : 21
                : 72
                Affiliations
                GRID grid.411680.a, ISNI 0000 0001 0514 4044, Key Laboratory of Xinjiang Endemic and Ethnic Diseases/the First Affiliated Hospital, , Shihezi University School of Medicine, ; Shihezi, Xinjiang China
                Author information
                http://orcid.org/0000-0003-3128-7780
                Article
                1762
                10.1186/s12935-021-01762-w
                7824949
                33482819
                4b00a71c-7926-409f-97e7-7857a08a092c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 November 2020
                : 6 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82060513
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82060297
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82060579
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81902850
                Funded by: Youth Science and Technology Innovation Leading Talents Project of Corps
                Award ID: 2020CB015
                Funded by: Youth Innovation Talents Project of Shihezi University
                Award ID: CXBJ201907
                Categories
                Primary Research
                Custom metadata
                © The Author(s) 2021

                Oncology & Radiotherapy
                liver cancer,cerna,biomarker,prognosis
                Oncology & Radiotherapy
                liver cancer, cerna, biomarker, prognosis

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