The development and experimental validation of hypoxia-related long noncoding RNAs prognostic signature in predicting prognosis and immunotherapy of cutaneous melanoma.

Cutaneous melanoma (CM) is widely acknowledged as a highly aggressive form of malignancy that is associated with a considerable degree of morbidity and poor prognosis. Despite this recognition, the precise role of hypoxia-related long noncoding RNAs (HRLs) in the pathogenesis of CM remains an area of active research. This study sought to elucidate the contribution of HRLs in CM by conducting a thorough screening and extraction of hypoxia-related genes (HRGs). In particular, we conducted univariate and multivariate Cox regression analyses to assess the independence of the prognostic signature of HRLs. Our results demonstrated that a novel risk model could be established based on five prognostic HRLs. Remarkably, patients with low-risk scores exhibited significantly higher overall survival rates compared to their high-risk counterparts, as confirmed by Kaplan-Meier survival analysis. Furthermore, we utilized consensus clustering analysis to categorize CM patients into two distinct subtypes, which revealed marked differences in their prognosis and immune infiltration landscapes. Our nomogram results confirmed that the HRLs prognostic signature served as an independent prognostic indicator, offering an accurate evaluation of the survival probability of CM patients. Notably, our findings from ESTIMATE and ssGSEA analyses highlighted significant disparities in the immune infiltration landscape between low- and high-risk groups of CM patients. Additionally, IPS and TIDE results suggested that CM patients in different risk subtypes may exhibit favorable responses to immunotherapy. Enrichment analysis and GSVA results indicated that immune-related signaling pathways may mediate the role of HRLs in CM. Finally, our tumor mutation burden (TMB) results indicated that patients with low-risk scores had a higher TMB status. In summary, the establishment of a risk model based on HRLs in this study provided an accurate prognostic prediction and correlated with the immune infiltration landscape of CM, thereby providing novel insights for the future clinical management of this disease.