Leveraging single‐cell sequencing analysis and bulk‐RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis

Author:

Xie Jiaheng1ORCID,Zhang Pengpeng2,Tang Qikai3,Ma Chenfeng4,Li Muyang5,Qi Min16

Affiliation:

1. Department of Plastic Surgery, Xiangya Hospital Central South University Changsha China

2. 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

3. Department of Neurosurgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School Nanjing University Nanjing China

4. Department of Neurosurgery The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital Nanjing Jiangsu China

5. Xiangya School of Medicine Central South University Changsha China

6. Department of Burns and Plastic Surgery Shenzhen Hospital of Southern Medical University Shenzhen China

Abstract

AbstractCutaneous 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.

Publisher

Wiley

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