A relative methylation ordering biomarker of lactylation-related genes predicts prognosis and therapeutic response in cutaneous melanoma.

Cutaneous melanoma (CM) is highly invasive with limited prognostic tools, and the prognostic relevance of lactylation, a novel post-translational modification, remains unexplored. We developed a lactylation-related prognostic model using relative methylation orderings from The Cancer Genome Atlas (TCGA) melanoma cohort (n = 458), which was validated in two dependent cohorts (n = 241). Comprehensive analyses of immune infiltration, therapy efficacy, drug sensitivity, and tumour mutation burden were conducted between high- and low-risk groups. Results showed that 284 lactylation-related genes collected classified TCGA melanoma patients into two clusters with significant survival differences (Kaplan-Meier log-rank test, p = 0.046). Between the two clusters, 279 differentially methylated loci were identified (false discovery rate < 0.05 and |Δβ| >0.1). Based on these loci, 1,743 locus pairs were significantly associated with overall survival (Univariate Cox regression, false discovery rate < 0.05). From these significant pairs, the CM 11 locus-pair prognostic model (CM-LP11) consisting of 11 pairs was constructed, which could effectively stratify patients into high- and low-risk groups with significantly different overall survival across all datasets (all p < 0.05, log-rank test). The high-risk group exhibited an immunosuppressive microenvironment with reduced CD8+ T cells and B cells and increased monocytes (p < 0.05, Wilcoxon test). Functional analysis of methylation differences between risk groups showed significant immune response-related pathways such as antigen processing and presentation. The high- and low-risk patients also exhibited distinct sensitivity to cytotoxic chemotherapy, targeted therapy, and immunotherapy, with the low-risk group showing higher immunophenoscore, cytolytic activity, and tumor mutational burden. CM-LP11 represents a robust lactylation-related prognostic biomarker that predictive survival outcomes and therapeutic responses in CM patients, proving insights for personalized treatment strategies.