Hounye Alphonse Houssou, Hu Bingqian, Wang Zheng, Wang Jiaoju, Cao Cong, Zhang Jianglin, Hou Muzhou, Qi Min
School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
Department of Dermatology, Xiangya Hospital of Central South University, Changsha, 410000, China.
J Mol Med (Berl). 2023 Oct;101(10):1267-1287. doi: 10.1007/s00109-023-02365-w. Epub 2023 Aug 31.
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.
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免疫治疗在ER相关风险评分定义的黑色素瘤亚组中的免疫特征和益处。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），使用单变量Cox模型和LASSO惩罚Cox模型选择了365个差异表达的ER相关基因。通过多变量Cox回归方法鉴定了10个影响总生存期（OS）的基因，以构建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、拷贝数变异（CNV）、甲基化、药物敏感性以及抗癌免疫检查点抑制剂（ICI）治疗的临床益处。我们通过与其他肿瘤类型进行比较，使用泛癌分析进一步验证了基因特征。基于芳香烃受体核转运蛋白样1（ARNTL）、AGO1、硫氧还蛋白（TXN）、sortilin 1（SORL1）、染色质域解旋酶DNA结合蛋白7（CHD7）、表皮生长因子受体（EGFR）、原癌基因c-KIT、人白细胞抗原DRB1（HLA-DRB1）、钾通道蛋白家族成员2（KCNA2）和内皮素受体B（EDNRB）基因构建了ER相关风险评分。高ER应激相关风险评分组患者的总生存期（OS）比低风险评分组患者差，这与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信号传导、CD4 T细胞、M1巨噬细胞、幼稚B细胞、自然杀伤（NK）细胞和嗜酸性粒细胞相关，并且从ICI治疗中获益较少。基于ER应激相关基因的表达模式，我们创建了一个合适的预测模型，该模型还可以帮助区分免疫特征、CNV、甲基化以及ICI治疗的临床益处。关键信息：黑色素瘤是一种恶性程度高、死亡率最高且预后极差的皮肤肿瘤。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使用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的预后具有出色的预测能力。
