Development of anoikis-related long non-coding RNA signature associated with prognosis and immune landscape in cutaneous melanoma patients

Background: Anoikis is involved in many critical biological processes in tumors; however, function in CM is still unknown. In this study, the relevance between Anoikis-related lncRNAs (ARLs) and the clinicopathological characteristics of patients with CM was comprehensively assessed. Methods: Through analysis of TCGA dataset, ARLs were identified by using TCGA dataset. Based on the ARLs, a risk model was established to anticipate the prognosis of patients with CM, besides, the prediction accuracy of the model was evaluated. The immune infiltration landscape of patients with CM was assessed comprehensively, and the correlation between ARLs and immunity was elucidated. Immunotherapy and drug sensitivity analyses were applied to analyze the treatment response in patients with CM with diverse risk scores. Different subgroups were distinguished among the patients using consensus cluster analysis. Results: A risk model based on six ARLs was set up to obtain an accurate prediction of the prognosis of patients with CM. There were distinctions in the immune landscape among CM patients with diverse risk scores and subgroups. Six prognosis-related ARLs were highly correlated with the number of immune cells. Patients with CM with different risk scores have various sensitivities to immunotherapy and antitumor drug treatments. Conclusion: Our newly risk model associated with ARLs has considerable prognostic value for patients with CM. Not only has the risk model high prediction accuracy but it also indicates the immune status of CM patients, which will provide a new direction for the individualized therapy of patients with CM.