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      Comprehensive molecular profiling of UV-induced metastatic melanoma in Nme1/ Nme2-deficient mice reveals novel markers of survival in human patients

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          Abstract

          Hepatocyte growth factor-overexpressing mice that harbor a deletion of the Ink4a/ p16 locus (HP mice) form melanomas with low metastatic potential in response to UV irradiation. Here we report that these tumors become highly metastatic following hemizygous deletion of the Nme1 and Nme2 metastasis suppressor genes (HPN mice). Whole genome sequencing of melanomas from HPN mice revealed a striking increase in lung metastatic activity that is associated with missense mutations in eight signature genes ( Arhgap35, Atp8b4, Brca1, Ift172, Kif21b, Nckap5, Pcdha2 and Zfp869). RNA-seq analysis of transcriptomes from HP and HPN primary melanomas identified a 32-gene signature (HPN lung metastasis signature) for which decreased expression is strongly associated with lung metastatic potential. Analysis of transcriptome data from The Cancer Genome Atlas revealed expression profiles of these genes that predict improved survival of patients with cutaneous or uveal melanoma. Silencing of three representative HPN lung metastasis signature genes ( ARRDC3, NYNRIN, RND3) in human melanoma cells resulted in increased invasive activity, consistent with roles for these genes as mediators of the metastasis suppressor function of NME1 and NME2. In conclusion, our studies have identified a family of genes that mediate suppression of melanoma lung metastasis, and which may serve as prognostic markers and/or therapeutic targets for clinical management of metastatic melanoma.

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          Most cited references78

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Cancer statistics, 2019

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, 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, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006-2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007-2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2-fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012-2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.
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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Journal
                8711562
                6325
                Oncogene
                Oncogene
                Oncogene
                0950-9232
                1476-5594
                19 August 2021
                25 August 2021
                November 2021
                25 February 2022
                : 40
                : 45
                : 6329-6342
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland-Baltimore, Baltimore, Maryland
                [2 ]Marlene and Stewart Greenebaum Comprehensive Cancer Center, School of Medicine, University of Maryland-Baltimore, Baltimore, Maryland
                [3 ]Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, California
                [4 ]Institute for Genome Sciences, School of Medicine, University of Maryland-Baltimore, Baltimore, Maryland
                [5 ]Department of Dermatology, University of Alabama at Birmingham, Birmingham, Alabama
                [6 ]The George Washington University Medical Center, Washington, DC
                [7 ]Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
                [8 ]Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama
                [9 ]Research and Development Service, VA Maryland Health Care System, Baltimore, Maryland
                Author notes
                [✝]

                Present address: American Association for Cancer Research, Philadelphia, PA, USA.

                Author contributions

                Conceptualization: JDC and DMK. Methodology: MKL, ECD-F, FPN, MGW, DWC, ZM, GM, RLE, and DMK. Formal analysis: MKL, ATS, ZM, and DMK. Investigation: MKL, GSP, NP, DS, GA, YX, EK, YJ, NM, MN, RMS, ACS, C-PD, MR, ATS, MGW, and DMK. Data curation: MKL, AM, ACS, and DMK. Writing (original draft): DMK; Writing (review and editing): MKL, RLE, GM, JDC, ZM, and DMK. Visualization: ATS, ZM, and DMK. Supervision: JDC, ZM, ATS, RLE, and DMK. Project administration: RLE, JDC, ZM, and DMK. Funding acquisition: RLE and DMK.

                [#]

                Edward C. De Fabo is deceased. Permission to include Dr. DeFabo as a co-author was provided by co-author Dr. Frances Noonan, his next-of-kin (wife).

                [* ] Corresponding Author: David M. Kaetzel, Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland-Baltimore, Baltimore, MD 21201. Phone: 410-706-5080; Fax: 410-706-8297; DKaetzel@ 123456som.umaryland.edu
                Article
                NIHMS1734128
                10.1038/s41388-021-01998-w
                8595820
                34433909
                8fb1da4e-b2b3-4724-8595-e3d3599333a4

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                Categories
                Article

                Oncology & Radiotherapy
                melanoma,metastasis suppressor,ultraviolet light,whole genome sequencing,rna-seq

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