Lipid metabolism-related molecular typing and prognostic characteristics of cutaneous melanoma

Skin cutaneous melanoma (SKCM) represents a highly aggressive malignancy with marked metastatic potential. Despite significant advances in targeted therapy and immunotherapy, the development of drug resistance remains a major clinical challenge. Emerging evidence indicates that lipid metabolic reprogramming plays a pivotal role in tumor progression; however, its specific regulatory mechanisms and clinical translational value in SKCM remain poorly understood. This study aims to systematically characterize the lipid metabolic features of SKCM and establish a robust prognostic prediction model.

We integrated transcriptomic and clinical data from University of Cingifornia Sisha Cruz Xena (UCSC Xena) and Gene Expression Omnibus (GEO) databases, identified lipid metabolism-related genes via GeneCards, and conducted differential expression analysis with limma. We then built a weighted gene co-expression network using weighted gene co-expression network analysis (WGCNA), determined molecular subtypes through consensus clustering, and developed a prognostic signature by combining multiple machine learning algorithms [random forest, least absolute shrinkage and selection operator (LASSO), and Cox regression]. Additionally, we characterized the tumor immune microenvironment using CIBERSORT, ESTIMATE, and Tumor Immune Dysfunction and Exclusion (TIDE), and predicted potential drug sensitivity based on the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Using eight key lipid metabolism-related differentially expressed genes (LMDEGs), we identified two molecular subtypes (Cluster A and B) in cutaneous melanoma. Notably, Cluster B exhibited significantly poorer overall survival and more aggressive clinicopathological characteristics. We subsequently established a novel lipid metabolism-related molecular (LMM) scoring system with four signature genes. Analysis of the tumor immune microenvironment revealed that patients with low LMM scores showed greater infiltration of CD8⁺ T cells and M1 macrophages, along with upregulated expression of multiple immune checkpoint molecules. Validation across multiple independent cohorts confirmed that the LMM score robustly predicted both clinical outcomes and immunotherapy responses in melanoma patients. Clinically, the LMM score also correlated with chemotherapy and targeted drug sensitivity, thereby offering a promising tool for personalized treatment stratification.

This study systematically delineates the lipid metabolic heterogeneity of SKCM for the first time. The proposed LMM scoring system demonstrates dual clinical utility in both prognostic evaluation and therapeutic guidance, providing a novel and reliable biomarker for precision melanoma management.