Abstract
AIM: To identify distinct disulfidptosis-molecular subtypes and develop a novel prognostic signature. METHODS/MATERIALS: We integrated into this study multiple SKCM transcriptomic datasets from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The consensus clustering algorithm was applied to categorize SKCM patients into different DRG subtypes. RESULTS: Three distinct DRG subtypes were identified, which were correlated to different clinical outcomes and signaling pathways. Then, a disulfidptosis-relaed signature and nomogram were constructed, which could accurately predict the individual OS of patients with SKCM. The high-risk group was less sensitive to immunotherapy than the low-risk group. CONCLUSION: The signature can assist healthcare professionals in making more accurate and individualized treatment choices for patients with SKCM.