Abstract
BACKGROUND: Natural killer (NK) cells, as key effectors of the innate immune system, play a crucial role in cancer prognosis and immunotherapy. This study aimed to develop a predictive model based on NK cell-associated immune genes (NKIGs) for patients with cutaneous melanoma (CM) and to assess its utility in predicting prognosis and guiding personalized treatment. METHODS: RNA-sequencing data and clinical information of CM patients were retrieved from The Cancer Genome Atlas (TCGA) database, while expression profiles from normal skin specimens were obtained from the Genotype-Tissue Expression (GTEx) database. Single-cell transcriptomes from CM were analyzed in conjunction with immune genes to identify NKIGs. These genes were then utilized to construct a prognostic model using univariate Cox regression and LASSO-Cox analysis. The model's accuracy and efficacy were evaluated through various statistical methods. Immunological characteristics, as well as the effectiveness of immunotherapy and chemotherapy in groups defined by the NKIGs signature, were explored. Additionally, the expression levels of NKIGs were validated via RT-qPCR in vitro experiments. RESULTS: The overall survival (OS) of patients in the low-risk group, as determined by the NKIGs signature, was significantly better than the high-risk group. The low-risk group also showed higher immune cell infiltration, particularly of CD8 T cells and activated CD4 memory T cells. CONCLUSIONS: This study has established a novel NKIGs signature with significant potential for predicting prognosis and guiding personalized therapeutic approaches in CM patients.