[Machine learning-based programmed cell death signature model for precise prediction of prognosis and treatment response in melanoma].

objectivesThe occurrence, metastasis, and drug resistance of melanoma pose major challenges to patient prognosis, and predictive models capable of accurately forecasting patient outcomes and guiding treatment are still lacking. This study aims to develop predictive models for melanoma prognosis and drug sensitivity based on mechanisms of programmed cell death (PCD).

methodsGenes related to 19 PCD patterns were collected and integrated from gene set enrichment analysis (GSEA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), relevant reviews, and published studies to establish a comprehensive PCD signature gene set. Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) cohort were obtained, and 3 untreated Gene Expression Omnibus (GEO) datasets (GSE65904, GSE19234, and GSE100797) were included as external validation cohorts. In addition, immunotherapy cohorts PRJEB23709, GSE136961, and GSE215222 were collected to validate the predictive value for immunotherapy. Single-cell transcriptomic data (GSE115978 and GSE215120) were processed using Seurat for quality control, normalization, dimensionality reduction, clustering, and cell annotation; spatial transcriptomic data were obtained from 10× Genomics and combined with Cottrazm for spatial partitioning and SpaCET deconvolution. Cell-cell communication was evaluated using CellChat to assess secreted signaling, extracellular matrix (ECM)-receptor interactions, and cell contact-mediated communication patterns. Based on the TCGA training set, a machine learning strategy comprising 10 algorithms and 101 combinations was used to construct PCD-related prognostic signatures, and the optimal model was selected using the average concordance index (C-index) across multiple cohorts. Differential analysis, GSEA, CIBERSORT, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) were further applied to evaluate immune infiltration, calculate T-cell receptor (TCR) clonal diversity, cytolytic activity, and T-cell effector gene expression profiles, and to explore the association between the PCD score (PCDS) and drug sensitivity.

resultsThe activities of the 19 PCD pathways differed significantly between normal skin and SKCM, suggesting that dysregulation is involved in melanoma progression. Mutation analysis showed that titin (

conclusionsPCDS is a cross-cohort robust tool for predicting melanoma prognosis and immunotherapy benefit, reflecting the degree of immunosuppression in the tumor immune microenvironment and myeloid/macrophage-related immunoregulatory features, and provides a basis for individualized risk stratification and potential drug selection. This study provides an in-depth elucidation of the regulatory mechanisms of PCD in the tumor immune microenvironment and offers an important theoretical foundation for personalized treatment decision-making in melanoma patients.