Development of a novel risk model for stratifying skin cutaneous melanoma patients based on prognostic senescence-related genes

Introduction: Skin cutaneous melanoma (SKCM) is a highly lethal skin carcinoma. Cellular senescence has a dual effect on tumor progression. Objective: This study investigates whether senescence-related genes can guide patient stratification by examining their association with survival outcomes in SKCM. Methods: Univariate and multivariate Cox regression analyses were performed to identify prognostic senescence-related genes. Based on these genes, samples were classified into two subtypes using consensus unsupervised clustering. Kaplan-Meier survival analysis was conducted, with statistical significance assessed via the log-rank test. Immune infiltration patterns were assessed using CIBERSORT. Finally, a prognostic risk model was constructed using Lasso regression following by multivariate Cox regression. Results: Based on the prognostic senescence-related genes, samples were classified into two subtypes with distinct survival outcomes and immune profiles. Cluster 2, linked to improved survival and enriched in cytokine-cytokine receptor interactions, showed higher infiltration of activated natural killer (NK) cells, CD8+ T cells as well as CD4+ memory T cells, along with enhanced immune pathway activation compared to cluster 1. Subsequently, a risk model was constructed based on the identified key genes and validated using both internal and external datasets. Stratification of patients by the median risk score showed that the low-risk cohort had a significantly better prognosis, with a favorable immune microenvironment enriched in CD8+ T cells, anti-tumor M1 macrophages, and activated NK cells. Conclusion: Senescence-related gene expression effectively stratified SKCM patients into subtypes with distinct survival outcomes and immune microenvironment profiles, offering new insights for the development of personalized medicine.