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      Identification of microRNA hsa-miR-30c-5p as an inhibitory factor in the progression of hepatocellular carcinoma and investigation of its regulatory network via comprehensive analysis

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          ABSTRACT

          Hepatocellular carcinoma (HCC) is a primary liver cancer with high morbidity and mortality. An increasing number of abnormal gene expressions were identified to be associated with the progression of HCC. Previous studies showed that the hsa-miR-30 c-5p (miR-30 c), one of the miR-30 family members, might play a role in suppressing tumor progression in a variety of tumors. The present study aims to examine miR-30 c effects in the development of HCC. The role of miR-30 c in HCC was comprehensively investigated by using bioinformatics and experiments in vitro. The multiple databases were combined to predict and screen the target genes and upstream lncRNAs of miR-30 c, and then constructed a competitive endogenous RNA (ceRNA) regulatory network with miR-30 c as the central miRNA. The miR-30 c-related ceRNA regulatory network was also initially validated in vitro. The results showed that miR-30 c over-expression could inhibit proliferation, migration, invasion, induce apoptosis, and increase G0/G1 phase ratio of HCC cells. Three miR-30 c upstream lncRNAs and 12 miR-30 c target genes were expressed in HCC cells with increased expression and poor prognosis, and a miR-30 C-related ceRNA regulatory network was constructed. This study verified miR-30 c as an inhibitory factor in the progression of HCC and performed analyses on the miR-30 c regulatory network, which might provide potential target information for HCC prognoses and therapies. However, further experiments in vivo and studies including clinical trials will be conducted to validate our results.

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          Most cited references43

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Predicting effective microRNA target sites in mammalian mRNAs

            MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. DOI: http://dx.doi.org/10.7554/eLife.05005.001
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              miRDB: an online database for prediction of functional microRNA targets

              Abstract MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.
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                Author and article information

                Journal
                Bioengineered
                Bioengineered
                Bioengineered
                Taylor & Francis
                2165-5979
                2165-5987
                21 September 2021
                2021
                21 September 2021
                : 12
                : 1
                : 7154-7166
                Affiliations
                [a ]School of Laboratory Medicine, Research Center of Clinical Laboratory Science, School of Laboratory Medicine, Bengbu Medical College; , Bengbu, China
                [b ]School of Life Science, Bengbu Medical College; , Bengbu, China
                [c ]Anhui Province Key Laboratory of Translational Cancer Research, Bengbu Medical College; , Bengbu, China
                [d ]Department of Infectious Diseases, First Affiliated Hospital of Bengbu Medical College, Bengbu Medical College; , Bengbu, China
                [e ]National Clinical Research Center for Infectious Diseases, First Affiliated Hospital of Bengbu Medical College, Bengbu Medical College; , Bengbu, China
                Author notes
                [* ]CONTACT Yu Gao gaoyu@ 123456bbmc.edu.cn School of Life Science, Anhui Province Key Laboratory of Translational Cancer Research, Bengbu Medical College; , No. 2600 Donghai Road, Bengbu, 233030, China. +86-19105521190
                Author information
                https://orcid.org/0000-0002-4210-7338
                Article
                1979439
                10.1080/21655979.2021.1979439
                8806565
                34503377
                2c8b6da7-9c14-4b87-b4c6-bb95a68256f0
                © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 5, Tables: 1, References: 43, Pages: 13
                Categories
                Research Article
                Research Paper

                Biomedical engineering
                hsa-mir-30c-5p,hepatocellular carcinoma,mirna,lncrnas,cerna regulatory network,inhibitory factor

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