IntroductionMelanoma (MM), the deadliest form of skin cancer, originates from melanocytes. Despite advances in immunotherapy that have somewhat improved the prognosis for MM patients, high levels of resistance to treatment continue to result in poor clinical outcomes. Identifying novel biomarkers and therapeutic targets is critical for improving the prognosis and treatment of MM.MethodsIn this study, we analyzed the expression patterns of WNT signaling pathway genes in MM and explored their potential mechanisms. Using Cox regression analysis, we identified 19 prognostic-related genes. Consistency clustering was performed to evaluate the potential of these genes as classifiers for prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was then applied to refine the gene set and construct a 13-gene prognostic model. We validated the model at multiple time points to assess its predictive performance. Additionally, correlation analyses were performed to investigate the relationships between key genes and processes, including epithelial-to-mesenchymal transition (EMT) and immune responses.ResultsWe identified that CSNK1E and RAC3 were significantly positively correlated with the EMT process, with CSNK1E showing a similar expression trend to EMT-related genes. Both genes were also negatively correlated with multiple immune cell types and immune checkpoint genes. The 13-gene prognostic model demonstrated excellent predictive performance in MM prognosis. Pan-cancer analysis further revealed heterogeneous expression patterns and prognostic potential of CSNK1E across various cancers. Wet experiments confirmed that CSNK1E promotes MM cell proliferation, invasion, and migration, and enhances malignant progression through the TGF-β signaling pathway.DiscussionOur findings suggest that CSNK1E plays a crucial role in MM progression and could serve as a potential therapeutic target. The WNT and TGF-β pathways may work synergistically in regulating the EMT process in MM, highlighting their potential as novel therapeutic targets. These insights may contribute to the development of more effective treatments for MM, particularly for overcoming resistance to current therapies.
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