Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, China.
Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Exp Dermatol. 2024 Jul;33(7):e15148. doi: 10.1111/exd.15148.
Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.
皮肤黑色素瘤是一种黑素细胞的恶性肿瘤，由于其侵袭性和全球发病率的上升，带来了重大挑战。尽管在治疗方面取得了进展，但患者反应的可变性突显了需要进一步研究新的治疗靶点的必要性，包括程序性细胞死亡途径（如坏死性凋亡）的作用。用于分析的黑色素瘤数据集 GSE215120、GSE19234、GSE22153 和 GSE65904 从 GEO 数据库下载。从 UCSC 网站下载 TCGA 的黑色素瘤数据。使用单细胞测序，我们评估了皮肤黑色素瘤中坏死性凋亡的异质性，确定了不同的细胞簇和与坏死性凋亡相关的基因表达模式。使用 101 种机器学习算法组合，基于与坏死性凋亡相关的关键基因构建了一个坏死性凋亡相关特征（NRS）。在四个队列（一个 TCGA 和三个 GEO 队列）中评估了 NRS 的预后价值，并分析了肿瘤微环境（TME），以了解坏死性凋亡、肿瘤突变负担（TMB）和免疫浸润之间的关系。最后，我们关注关键靶标 TSPAN10 在皮肤黑色素瘤的预后、发病机制、免疫治疗相关性和药物敏感性中的作用。我们的研究揭示了黑色素瘤细胞中坏死性凋亡的显著异质性，上皮细胞、髓样细胞和成纤维细胞中更为常见。通过严格的机器学习技术开发的 NRS 显示出强大的预后能力，在所有队列中都能区分预后较差的高危患者。TME 分析表明，高 NRS 评分与较低的 TMB 和免疫细胞浸润减少相关，表明坏死性凋亡影响黑色素瘤进展的潜在机制。最后，TSPAN10 已被确定为皮肤黑色素瘤的关键靶标，与预后不良高度相关。这些发现突显了坏死性凋亡在皮肤黑色素瘤中的复杂作用，并引入了 NRS 作为一种新的预后工具，具有指导治疗决策的潜力。
