A Mitoxyperilysis-Related Single-Cell and Machine-Learning Framework Defines an Immune-Cold Melanoma Phenotype and a Robust Prognostic Signature.

Mitoxyperilysis is a mitochondria-dependent membrane lysis process driven by innate immune and metabolic cues, yet its clinical relevance in melanoma remains unclear. We analyzed single-cell RNA-seq data (GSE215120) to quantify a mitoxyperilysis-related score (MRS), resolve cell-type heterogeneity, and compare predicted cell-cell communication between MRS-high and MRS-low tumor states. MRS was robust across alternative scoring approaches and varied markedly across cell types and malignant subpopulations. Compared with MRS-low tumors, the MRS-high state exhibited increased predicted intercellular communication (740 vs. 448 interactions) and higher global interaction strength (18,989 vs. 10,652), suggesting a rewired tumor ecosystem. To translate these programs to bulk melanoma, we selected the top 150 genes most correlated with MRS and benchmarked 101 machine-learning strategies in TCGA-SKCM to derive prognostic models, followed by external validation in six independent GEO cohorts. A gradient boosting machine (GBM)-based signature showed the most consistent cross-cohort performance and reliably stratified overall survival. High riskScore was associated with reduced immune and stromal signals, higher tumor purity, and an immune-cold tumor microenvironment as estimated by multialgorithm deconvolution and ESTIMATE. As a representative model gene, GPR143 was upregulated in melanoma, was associated with worse survival, and its functional knockdown suppressed colony formation in melanoma cells. Collectively, this work establishes a novel integrative framework that-for the first time-connects single-cell-resolved mitoxyperilysis-associated transcriptional programs with large-scale multicohort machine-learning validation, thereby enabling both mechanistic interpretation of immunometabolic heterogeneity and clinically applicable risk stratification in melanoma.