A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma

Melanoma is a highly aggressive cutaneous malignancy characterised by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multi-omics data (TCGA, GEO, ENA) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. Cox regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A Metabolism-Related Prognostic Model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across four cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene promoting melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1 and PSMD14-mediated stabilisation of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth via SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.