Construction and validation of a lipid metabolism-related genes prognostic signature for skin cutaneous melanoma

Background: Skin cutaneous melanoma (SKCM) is a highly aggressive form of skin cancer, characterized by a poor prognosis, particularly in advanced stages. Emerging evidence has underscored the pivotal role of lipid metabolism in cancer progression, influencing tumor growth, metastasis, and therapeutic resistance. However, the potential of lipid metabolism-related genes (LMGs) as prognostic biomarkers in SKCM remains largely unexplored. This study aims to investigate the functional roles and prognostic significance of LMGs in patients with SKCM. Methods: A total of 776 LMGs were obtained from the Molecular Signature Database (MSigDB). mRNA sequencing data and corresponding clinical follow-up information for SKCM were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on LMGs expression profiles, the non-negative matrix factorization (NMF) algorithm was applied to identify distinct molecular subtypes. Subsequently, weighted correlation network analysis (WGCNA) was performed to detect co-expressed gene modules associated with these subtypes. To construct a prognostic risk model, least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were conducted. The resulting prognostic signature was validated across multiple external cohorts (GSE65904 and GSE54467). Further analyses were carried out to compare immune cell infiltration levels, expression of immune checkpoint-related genes, and predicted immunotherapy responses between different risk groups. A nomogram integrating clinical factors and risk scores was developed to predict survival outcomes and assess the prognostic risk for SKCM patients. Additionally, genome mutation profiling and pan-cancer analyses based on the prognostic signature were performed to investigate its broader oncological relevance. The expression patterns of LMGs within the SKCM tumor microenvironment were further explored using single-cell RNA sequencing data from the GSE72056 dataset. Finally, functional validation of the most critical hub gene was conducted through both in vitro and in vivo experiments. Results: A 10 LMGs prognostic signature was successfully established and validated to predict survival outcomes in SKCM patients, serving as an independent prognostic factor for overall survival. This signature was significantly associated with immune cell infiltration, with the low-risk group exhibiting a higher abundance of antitumor immune cells compared to the high-risk group. Additionally, the low-risk group demonstrated elevated expression levels of key immune checkpoint genes, including PD-1, PD-L1, CTLA-4, and LAG3, along with higher Immunophenoscore (IPS), suggesting a more favorable immune microenvironment and potentially better responsiveness to immunotherapy. Moreover, functional studies further revealed that knockdown of CCNA2 inhibited melanoma cell proliferation, migration, and invasion in vitro, while suppressing tumorigenicity in vivo. These findings underscore the potential of CCNA2 as a novel therapeutic target in SKCM. Conclusion: In summary, this study successfully established a 10 LMGs prognostic signature for predicting survival outcomes and identified CCNA2 as a promising therapeutic target in SKCM.