肿瘤细胞治疗研究
英文原题：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.
基于癌症基因组图谱（TCGA）黑色素瘤数据集（n = 471）和GTEx数据库（n = 813），使用单因素Cox模型和LASSO惩罚Cox模型筛选出365个差异表达的ER相关基因。
我们旨在开发内质网（ER）应激相关风险特征，以预测黑色素瘤的预后，并基于机器学习算法阐明ER相关风险评分定义的黑色素瘤亚组中的免疫特征和免疫治疗获益。基于癌症基因组图谱（TCGA）黑色素瘤数据集（n = 471）和GTEx数据库（n = 813），使用单因素Cox模型和LASSO惩罚Cox模型筛选出365个差异表达的ER相关基因。通过多因素Cox回归方法鉴定出10个影响OS的基因，构建ER相关特征，并使用基因表达综合数据库（GEO）数据集进行验证。此后，分析了风险评分亚组中的免疫特征、CNV、甲基化、药物敏感性以及抗癌免疫检查点抑制剂（ICI）治疗的临床获益。我们进一步通过泛癌分析将该基因特征与其他肿瘤类型进行比较验证。ER相关风险评分基于ARNTL、AGO1、TXN、SORL1、CHD7、EGFR、KIT、HLA-DRB1、KCNA2和EDNRB基因构建。高ER应激相关风险评分组患者的总生存期（OS）低于低风险评分组患者，与GEO队列中的结果一致。综合结果表明，高ER应激相关风险评分与细胞黏附、γ吞噬作用、阳离子转运、细胞表面细胞黏附、KRAS信号通路、CD4 T细胞、M1巨噬细胞、初始B细胞、自然杀伤（NK）细胞和嗜酸性粒细胞相关，且从ICI治疗中获益较少。基于内质网应激相关基因的表达模式，我们构建了一个合适的预测模型，该模型还可有助于区分免疫特征、CNV、甲基化以及ICI治疗的临床获益。关键信息：黑色素瘤是恶性程度高、致死率最高且预后极差的皮肤肿瘤。在使用包含更多特征的模型时，应考虑模型的实用性。我们基于机器学习算法，利用TCGA和GEO数据库构建了内质网应激相关特征。内质网应激相关特征对黑色素瘤预后具有出色的预测能力。
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. 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, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. 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. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.
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