A prognostic model for melanoma patients on the basis of immune-related lncRNAs

Yao Wang, Hong Jun Ba, Xi Zhi Wen, Min Zhou, Can Küçük, Luca Tamagnone, Li Wei, Hua You

Research output: Contribution to journalArticlepeer-review


The prognosis of melanoma patients is highly variable due to multiple factors conditioning immune response and driving metastatic progression. In this study, we have correlated the expression of immune-related lncRNAs with patient survival, developed a prognostic model, and investigated the characteristics of immune response in the diverse groups. The gene expression profiles and prognostic information of 470 melanoma patients were downloaded from TCGA database. Significantly predictive lncRNAs were identified by multivariate Cox regression analyses, and a prognostic model based on these variables was constructed to predict survival. Kaplan-Meier curves were plotted to estimate overall survival. The predictive accuracy of the model was evaluated by the area under the ROC curve (AUC). Principal component analysis was used to observe the distribution of immune-related genes. CIBERSORT and ESTIMATE were used to evaluate the composition of immune cells and the immune microenvironment. Eight immune-related lncRNAs were determined to be prognostic by multivariate COX regression analysis. The patient scores were calculated and divided into high- and low-risk groups. The model could effectively predict the prognosis in patients of different stages. The AUC of the model is 0.784, which was significantly higher than that of the other variables. There were significant differences in the distribution of immune-related genes between two groups; the immune score and immune function enrichment score were higher in the low risk group.

Original languageEnglish
Pages (from-to)6554-6564
Number of pages11
Issue number5
Publication statusPublished - Mar 15 2021


  • gene expression profile
  • immunotherapy
  • lncRNA
  • melanoma
  • prediction model

ASJC Scopus subject areas

  • Ageing
  • Cell Biology


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