Post-translational modification signature shapes the tumor immune microenvironment and predicts clinical outcomes in melanoma.

Post-translational modifications (PTMs) play a pivotal role in the initiation and progression of melanoma. To systematically evaluate their clinical significance, we integrated 10 widely used machine learning algorithms and 101 combinations to construct a PTM-related risk score model (PTMRS) based on 54 prognostic PTM genes. The model demonstrated robust prognostic predictive performance and stability across multiple melanoma cohorts (TCGA, GSE19234, GSE22153, GSE54467, and GSE65904). Immune profiling revealed that patients in the low-PTMRS group exhibited a more active immune microenvironment, characterized by increased infiltration of CD8⁺ T cells and M1 macrophages, along with reduced immune exclusion scores. Single-cell analysis further indicated that melanoma cells had the highest PTMRS scores, and distinct cell–cell communication patterns were observed between high and low PTMRS groups. ALG3, identified as a key gene positively correlated with PTMRS, was associated with poor prognosis and an immunosuppressive state. Immunohistochemistry and RT-qPCR results supported its potential oncogenic role in melanoma. Moreover, PTMRS was correlated with drug sensitivity across multiple compounds, suggesting its utility in guiding personalized therapeutic strategies. Collectively, the PTMRS model serves not only as an independent prognostic indicator for melanoma patients but also as a valuable tool for immune landscape assessment and targeted treatment decision-making.