Evaluation of immunological characteristics and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatic analysis

Abstract Purpose: We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Methods: Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n=813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and Lasso penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups were analysed. Results: The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Conclusion: Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics and the clinical benefit of ICI therapy.