The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning

Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to elucidate the role of mitophagy-related genes (MRGs) in cutaneous melanoma (CM) through single-cell RNA sequencing (scRNA-seq) and machine learning approaches, ultimately developing a predictive model for patient prognosis. The scRNA-seq data, bulk transcriptomic data, and clinical data of CM were obtained from publicly available databases. The single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify gene modules associated with mitophagy phenotypes. A machine learning framework employing ten different algorithms was used to develop the prognostic model. Based on scRNA-seq data, we identified 16 distinct cell subpopulations in melanoma, and melanoma cells exhibited significantly higher mitophagy scores. The turquoise module identified via WGCNA showed the strongest correlation with mitophagy scores. A prognostic model incorporating seven genes was developed through machine learning algorithms, achieving an average C-index of 0.754 across training and validation cohorts. Functionally, low-risk patients were enriched in interferon-gamma response and inflammatory processes, whereas high-risk patients showed enrichment in glycolysis regulation and signaling pathways such as KRAS and Wnt/beta-catenin. Notably, low-risk patients demonstrated enhanced immune infiltration and greater sensitivity to immunotherapy. RT-qPCR validated the expression level of 7 model genes in human melanoma cell lines and normal melanocyte cell lines. Our study provides a comprehensive understanding of MRGs in melanoma and presents a novel prognostic model. These findings enhance our understanding of the tumor microenvironment and may guide personalized treatment strategies for CM patients.