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

Luca Tamagnone, Raul Sanchez-Vazquez, Ana Guío-Carrión, Antonio Zapatero-Gaviria, Paula Martínez, Maria A. Blasco

Risultato della ricerca: Contributo in rivistaArticolo in rivista

5 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)1-11
Numero di pagine11
RivistaAging
Volume13
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

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

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