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In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict metastatic events on the early\u2010stage population of patients. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with interleukin 4 (IL\u20104), IL\u20106, IL\u201010, IL\u201017A, interferon \u03b3 (IFN\u2010\u03b3), transforming growth factor\u2010\u03b2 (TGF\u2010 \u03b2), and granulocyte\u2013macrophage colony\u2010stimulating factor (GM\u2010CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I\u2010II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan\u2013Meier techniques to define an algorithm capable of accurately classifying early\u2010stage melanoma patients with a high and low risk of developing metastasis. The results show that in early\u2010stage melanoma patients, serum levels of the cytokines IL\u20104, GM\u2010CSF, and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis.<\/jats:p>","DOI":"10.1002\/1878-0261.12732","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T21:49:55Z","timestamp":1591134595000},"page":"1705-1718","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Serum markers improve current prediction of metastasis development in early\u2010stage melanoma patients: a machine learning\u2010based study"],"prefix":"10.1002","volume":"14","author":[{"given":"Filippo","family":"Mancuso","sequence":"first","affiliation":[{"name":"Department of Cell Biology and Histology Faculty of Medicine and Nursing UPV\/EHU Leioa Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Lage","sequence":"additional","affiliation":[{"name":"Department of Cell Biology and Histology 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