{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T13:48:46Z","timestamp":1790344126313,"version":"4.1.0"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, <jats:italic>ALDH1A1<\/jats:italic>, <jats:italic>HSP90AB1<\/jats:italic>, <jats:italic>KIT<\/jats:italic>, <jats:italic>KRT16<\/jats:italic>, <jats:italic>SPRR3<\/jats:italic> and <jats:italic>TMEM45B<\/jats:italic> whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, <jats:italic>HSP90AB1<\/jats:italic>, <jats:italic>SPRR3<\/jats:italic> and <jats:italic>KRT16<\/jats:italic> remained significant predictors of MSS in a joint analysis (HR\u2009=\u20092.3, P\u2009=\u20090.03) although, <jats:italic>HSP90AB1<\/jats:italic> (HR\u2009=\u20091.9, P\u2009=\u20092\u2009\u00d7\u200910<jats:sup>\u22124<\/jats:sup>) alone remained predictive after adjusting for clinical predictors.<\/jats:p>","DOI":"10.1038\/s41598-017-17330-0","type":"journal-article","created":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T11:32:26Z","timestamp":1512473546000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach"],"prefix":"10.1038","volume":"7","author":[{"given":"Rahul","family":"Metri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhilash","family":"Mohan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00e9r\u00e9mie","family":"Nsengimana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Pozniak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"Molina-Paris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Newton-Bishop","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8752-8785","authenticated-orcid":false,"given":"David","family":"Bishop","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagasuma","family":"Chandra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"17330_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.clindermatol.2009.06.012","volume":"28","author":"A Forschner","year":"2010","unstructured":"Forschner, A. et al. 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