全身炎症标志物与肿瘤微环境在预测皮肤黑色素瘤前哨淋巴结转移中的分离

Systemic inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), are established prognostic biomarkers in melanoma, particularly in advanced disease.

However, their utility in predicting early metastatic spread, such as sentinel lymph node (SLN) involvement, remains unclear. To evaluate the relative contributions of systemic inflammatory indices and tumor microenvironment (TME) features in predicting SLN metastasis in cutaneous melanoma. In this retrospective, single-center study, clinicopathological parameters, peripheral blood-derived inflammatory indices (NLR, PLR, lymphocyte-to-leukocyte ratio, lymphocyte-to-monocyte ratio, and neutrophil-to-eosinophil ratio), and TME features, including tumor-infiltrating lymphocytes (TILs) and tumor regression, were analyzed.

Multivariable analyses and nested predictive models were constructed to identify independent predictors and to assess the incremental predictive value of each parameter group. SLN positivity was strongly associated with established histopathological markers, including Breslow thickness, ulceration, mitotic rate, and lymphovascular invasion (all P < 0.

001). In contrast, systemic inflammatory indices showed no significant association with SLN involvement (all P > 0. 05). TME-related features demonstrated significant associations; higher TIL density showed a dose-dependent inverse relationship with SLN positivity (P = 0. 001), while tumor regression was linked to reduced nodal metastasis (P = 0. 031).

In addition, lower platelet counts were also associated with SLN positivity (P = 0. 03). Incorporation of TME features improved model performance, whereas systemic inflammatory markers did not provide additional predictive value. Early nodal metastasis in melanoma appears to be driven by local tumor-immune interactions rather than systemic inflammation.

These findings support the limited predictive value of circulating inflammatory markers and highlight the importance of TME features in risk stratification and clinical decision-making.