Research Paper Volume 13, Issue 5 pp 6554—6564
A prognostic model for melanoma patients on the basis of immune-related lncRNAs
- 1 Medical Oncology Department, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, Guangdong, China
- 2 Pediatric Cardiology Department, Heart Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
- 3 Biotherapy Center, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou 510060, Guangdong, China
- 4 İzmir Biomedicine and Genome Center (IBG), İzmir 35340, Turkey
- 5 İzmir International Biomedicine and Genome Institute (iBG-İzmir), Dokuz Eylül University, İzmir 35340, Turkey
- 6 Department of Medical Biology, Faculty of Medicine, Dokuz Eylül University, İzmir 35340, Turkey
- 7 Department Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome 00168, Italy
- 8 Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome 00168, Italy
Received: March 9, 2020 Accepted: February 12, 2021 Published: March 6, 2021https://doi.org/10.18632/aging.202730
How to Cite
Copyright: © 2021 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
ICIs: immune checkpoint inhibitors; lncRNA: long noncoding RNA; PCA: principal component analysis; TLRs: toll-like receptors; TMB: tumor mutation burden; TIL: tumor infiltrating lymphocyte; Indel: insertion and deletion mutation; TAM: tumor-associated macrophage; TCGA: The Cancer Genome Atlas; AIC: Akaike information criterion; TIICs: tumor-infiltrating immune cells; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumors using Expression data; OS: overall survival; AUC: the area under the ROC curve.