Department of Cell Biology and Histology, Faculty of Medicine and Nursing, UPV/EHU, Leioa, Spain.
Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.
Mol Oncol. 2020 Aug;14(8):1705-1718. doi: 10.1002/1878-0261.12732. Epub 2020 Jun 24.
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.
转移发展是黑色素瘤患者的重要威胁，即使在早期诊断并切除原发肿瘤后也是如此。在这种情况下，确定预后生物标志物将是非常有意义的。血清中包含有关机体一般状况的信息，因此是生物标志物的宝贵来源。因此，我们旨在定义可以与疾病的临床和组织病理学特征一起用于预测早期患者转移事件的血清生物标志物。我们之前已经证明，在 II 期黑色素瘤患者中，血清中 dermcidin（DCD）的水平与转移进展有关。基于免疫反应对癌症进展的重要性以及最近 DCD 与针对癌细胞的局部和全身免疫反应的关联，我们在另一批新的患者队列中分析了血清 DCD 以及白细胞介素 4（IL-4）、白细胞介素 6（IL-6）、白细胞介素 10（IL-10）、白细胞介素 17A（IL-17A）、干扰素 γ（IFN-γ）、转化生长因子-β（TGF-β）和粒细胞-巨噬细胞集落刺激因子（GM-CSF）。我们最初招募了 448 名黑色素瘤患者，其中 323 名根据 AJCC 诊断为 I 期- II 期。通过 ELISA 和 Luminex 测定选定细胞因子的水平，并利用机器学习和 Kaplan-Meier 技术分析获得的数据，以定义一种能够准确分类具有高转移风险和低转移风险的早期黑色素瘤患者的算法。结果表明，在早期黑色素瘤患者中，血清中细胞因子 IL-4、GM-CSF 和 DCD 的水平以及 Breslow 厚度是最能预测黑色素瘤转移的因素。此外，所得算法代表了一种新工具，可以区分预后良好的患者和未来转移风险高的患者。
