Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer 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 bene t of immunotherapy in ERrelated risk score-de ned 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 identi ed 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 bene t 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 bene tted 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 bene t of ICI therapy.