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We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM\u2010specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome\u2010wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non\u2010Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non\u2010Hispanic white patients with invasive CM. Four independent SNPs (<jats:italic>MMP16<\/jats:italic> rs10090371 C&gt;A, <jats:italic>ADAMTS3<\/jats:italic> rs788935 T&gt;C, <jats:italic>TLL2<\/jats:italic> rs10882807 T&gt;C and <jats:italic>MMP9<\/jats:italic> rs3918251 A&gt;G) were identified as predictors of CMSS, with a variant\u2010allele attributed hazards ratio (HR) of 1.73 (1.32\u20102.29, 9.68E\u201005), 1.46 (1.15\u20101.85, 0.002), 1.68 (1.31\u20102.14, 3.32E\u201005) and 0.67 (0.51\u20100.87, 0.003), respectively, in the meta\u2010analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose\u2010response manner as the number of risk genotypes increased (<jats:italic>P<\/jats:italic><jats:sub>trend\u2009<\/jats:sub>&lt;\u20090.001). An improvement was observed in the prediction model (area under the curve [AUC]\u2009=\u200981.4% vs. 78.6%), when these risk genotypes were added to the model containing non\u2010genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mc.22716","type":"journal-article","created":{"date-parts":[[2017,8,10]],"date-time":"2017-08-10T12:16:36Z","timestamp":1502367396000},"page":"22-31","source":"Crossref","is-referenced-by-count":8,"title":["Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival"],"prefix":"10.1002","volume":"57","author":[{"given":"Yinghui","family":"Xu","sequence":"first","affiliation":[{"name":"Cancer Center The First Hospital of Jilin University Changchun Jilin China"},{"name":"Department of Medicine Duke University School of Medicine Durham North Carolina"},{"name":"Duke Cancer Institute Duke University Medical Center Durham North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanru","family":"Wang","sequence":"additional","affiliation":[{"name":"Cancer Center The First Hospital of Jilin University Changchun Jilin China"},{"name":"Department of Medicine Duke University School of Medicine Durham North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Cancer Center The First Hospital of Jilin University Changchun Jilin China"},{"name":"Department of Medicine Duke University School of Medicine Durham North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Dermatology Xijing Hospital Xi'an Shanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dakai","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science Geisel School of Medicine, Dartmouth College Hanover New Hampshire"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher I.","family":"Amos","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science Geisel School of Medicine, Dartmouth College Hanover New Hampshire"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenying","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Surgical Oncology The University of Texas M. 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