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      Identification of Tumor Suppressive Genes Regulated by miR-31-5p and miR-31-3p in Head and Neck Squamous Cell Carcinoma

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          Abstract

          We identified the microRNA (miRNA) expression signature of head and neck squamous cell carcinoma (HNSCC) tissues by RNA sequencing, in which 168 miRNAs were significantly upregulated, including both strands of the miR-31 duplex ( miR-31-5p and miR-31-3p). The aims of this study were to identify networks of tumor suppressor genes regulated by miR-31-5p and miR-31-3p in HNSCC cells. Our functional assays showed that inhibition of miR-31-5p and miR-31-3p attenuated cancer cell malignant phenotypes (cell proliferation, migration, and invasion), suggesting that they had oncogenic potential in HNSCC cells. Our in silico analysis revealed 146 genes regulated by miR-31 in HNSCC cells. Among these targets, the low expression of seven genes ( miR-31-5p targets: CACNB2 and IL34; miR-31-3p targets: CGNL1, CNTN3, GAS7, HOPX, and PBX1) was closely associated with poor prognosis in HNSCC. According to multivariate Cox regression analyses, the expression levels of five of those genes ( CACNB2: p = 0.0189; IL34: p = 0.0425; CGNL1: p = 0.0014; CNTN3: p = 0.0304; and GAS7: p = 0.0412) were independent prognostic factors in patients with HNSCC. Our miRNA signature and miRNA-based approach will provide new insights into the molecular pathogenesis of HNSCC.

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          Most cited references43

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          Cancer Statistics, 2021

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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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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              GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

              Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                08 June 2021
                June 2021
                : 22
                : 12
                : 6199
                Affiliations
                [1 ]Department of Oral Science, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; Sachi.o8952@ 123456chiba-u.jp (S.O.); minemura@ 123456chiba-u.jp (C.M.); kasamatsua@ 123456faculty.chiba-u.jp (A.K.); uzawak@ 123456faculty.chiba-u.jp (K.U.)
                [2 ]Department of Functional Genomics, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; cada5015@ 123456chiba-u.jp (S.A.); t.kinoshita@ 123456chiba-u.jp (T.K.); caxa1117@ 123456chiba-u.jp (Y.G.); naoko-k@ 123456hospital.chiba-u.jp (N.K.)
                [3 ]Department of Otorhinolaryngology/Head and Neck Surgery, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; thanazawa@ 123456faculty.chiba-u.jp
                [4 ]Department of Biochemistry and Genetics, Chiba University Graduate School of Medicine, Chiba 260-8670, Japan; moriya.shogo@ 123456chiba-u.jp
                Author notes
                [* ]Correspondence: naoseki@ 123456faculty.chiba-u.jp ; Tel.: +81-43-226-2971; Fax: +81-43-227-3442
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-4731-7956
                https://orcid.org/0000-0002-7036-0859
                Article
                ijms-22-06199
                10.3390/ijms22126199
                8227492
                34201353
                c027f132-7389-4cd6-9eb2-7fd760d96cf6
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 25 April 2021
                : 05 June 2021
                Categories
                Article

                Molecular biology
                hnscc,mir-31-5p,mir-31-3p,microrna,oncogenic mirna,tumor suppressor
                Molecular biology
                hnscc, mir-31-5p, mir-31-3p, microrna, oncogenic mirna, tumor suppressor

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