Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study
Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the orga...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-08-01
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| Series: | Molecular Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/1878-0261.12732 |
| _version_ | 1818790860793315328 |
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| author | Filippo Mancuso Sergio Lage Javier Rasero José Luis Díaz‐Ramón Aintzane Apraiz Gorka Pérez‐Yarza Pilar Ariadna Ezkurra Cristina Penas Ana Sánchez‐Diez María Dolores García‐Vazquez Jesús Gardeazabal Rosa Izu Karmele Mujika Jesús Cortés Aintzane Asumendi María Dolores Boyano |
| author_facet | Filippo Mancuso Sergio Lage Javier Rasero José Luis Díaz‐Ramón Aintzane Apraiz Gorka Pérez‐Yarza Pilar Ariadna Ezkurra Cristina Penas Ana Sánchez‐Diez María Dolores García‐Vazquez Jesús Gardeazabal Rosa Izu Karmele Mujika Jesús Cortés Aintzane Asumendi María Dolores Boyano |
| author_sort | Filippo Mancuso |
| collection | DOAJ |
| description | Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. 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‐stage 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‐4), IL‐6, IL‐10, IL‐17A, interferon γ (IFN‐γ), transforming growth factor‐β (TGF‐ β), and granulocyte–macrophage colony‐stimulating factor (GM‐CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I‐II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan–Meier techniques to define an algorithm capable of accurately classifying early‐stage melanoma patients with a high and low risk of developing metastasis. The results show that in early‐stage melanoma patients, serum levels of the cytokines IL‐4, GM‐CSF, 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. |
| first_indexed | 2024-12-18T15:02:10Z |
| format | Article |
| id | doaj.art-9611a4d808f34cc49be327fa11536b75 |
| institution | Directory Open Access Journal |
| issn | 1574-7891 1878-0261 |
| language | English |
| last_indexed | 2024-12-18T15:02:10Z |
| publishDate | 2020-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Oncology |
| spelling | doaj.art-9611a4d808f34cc49be327fa11536b752022-12-21T21:03:52ZengWileyMolecular Oncology1574-78911878-02612020-08-011481705171810.1002/1878-0261.12732Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based studyFilippo Mancuso0Sergio Lage1Javier Rasero2José Luis Díaz‐Ramón3Aintzane Apraiz4Gorka Pérez‐Yarza5Pilar Ariadna Ezkurra6Cristina Penas7Ana Sánchez‐Diez8María Dolores García‐Vazquez9Jesús Gardeazabal10Rosa Izu11Karmele Mujika12Jesús Cortés13Aintzane Asumendi14María Dolores Boyano15Department of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainDepartment of Medical Oncology Onkologikoa Hospital Donostia SpainBiocruces Bizkaia Health Research Institute Barakaldo SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainDepartment of Cell Biology and Histology Faculty of Medicine and Nursing UPV/EHU Leioa SpainMetastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. 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‐stage 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‐4), IL‐6, IL‐10, IL‐17A, interferon γ (IFN‐γ), transforming growth factor‐β (TGF‐ β), and granulocyte–macrophage colony‐stimulating factor (GM‐CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I‐II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan–Meier techniques to define an algorithm capable of accurately classifying early‐stage melanoma patients with a high and low risk of developing metastasis. The results show that in early‐stage melanoma patients, serum levels of the cytokines IL‐4, GM‐CSF, 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.https://doi.org/10.1002/1878-0261.12732dermcidininterleukinsmelanomaprognosisserum biomarkers |
| spellingShingle | Filippo Mancuso Sergio Lage Javier Rasero José Luis Díaz‐Ramón Aintzane Apraiz Gorka Pérez‐Yarza Pilar Ariadna Ezkurra Cristina Penas Ana Sánchez‐Diez María Dolores García‐Vazquez Jesús Gardeazabal Rosa Izu Karmele Mujika Jesús Cortés Aintzane Asumendi María Dolores Boyano Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study Molecular Oncology dermcidin interleukins melanoma prognosis serum biomarkers |
| title | Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study |
| title_full | Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study |
| title_fullStr | Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study |
| title_full_unstemmed | Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study |
| title_short | Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study |
| title_sort | serum markers improve current prediction of metastasis development in early stage melanoma patients a machine learning based study |
| topic | dermcidin interleukins melanoma prognosis serum biomarkers |
| url | https://doi.org/10.1002/1878-0261.12732 |
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