Abstract：
OBJECTIVE: This study aimed to improve personalized treatment strategies and predict survival outcomes for patients with uveal melanoma (UM).
BACKGROUND: Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM.
OBJECTIVE: This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response.
METHODS: The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the \"Limma\" package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay.
RESULTS: The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736.
CONCLUSIONS: UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.
BACKGROUND: Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM.
OBJECTIVE: This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response.
METHODS: The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the \"Limma\" package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay.
RESULTS: The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736.
CONCLUSIONS: UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.
摘要：
目的：本研究旨在改善葡萄膜黑色素瘤（UM）患者的个性化治疗策略并预测其生存结果。
背景：拷贝数畸变（CNA）已被认为是转移性UM的主要特征。
目的：本研究旨在探索在UM分类中使用拷贝数变异（CNV）的可行性，预后分层和治疗反应。
方法：使用TCGA-UVM队列中的CNV数据对样品进行分类。通过“Limma”软件包筛选亚型之间的差异表达基因（DEGs）。通过对DEGs进行加权基因共表达网络分析(WGCNA)来鉴定与非整倍性评分相关的模块和中心基因。采用单变量Cox和最小绝对收缩和选择算子（LASSO）回归分析来训练枢纽基因，以开发UM的预后模型。最后,在UM细胞中验证了筛选的预后关键基因的表达水平，采用实时定量PCR(qRT-PCR)和transwell法检测细胞迁移和侵袭能力。
结果：UM样本分为3种CNV亚型，在总生存期（OS）和疾病特异性生存期（DSS）方面存在显着差异。C1的OS和DSS最短，免疫浸润水平最高。从这三种亚型共获得2036个DEG。通过WGCNA选择了与非整倍性评分最接近的80个hub基因。基于单变量Cox和LASSO回归分析最终确定8个基因为关键预后基因。包括HES6，RNASEH2C，NQO1、NUDT14、TTYH3、GJC1、FKBP10和MRPL24。使用这八个基因建立了一个预后模型，展示了强大的预测能力。不同风险组患者对免疫治疗的反应差异显著。我们发现高危患者对两种药物（Palbociclib_1054和Ribociclib_1632）更敏感，而低危患者对AZD1208_1449、ERK_2440_1713、Mirin_1048和Selumetinib_1736更敏感。
结论：本研究中的UM分为三种CNV亚型，并建立了基于8个非整倍体评分相关基因的模型来评估UM患者的预后和药物治疗效果。当前结果可能有助于UM管理的临床决策过程。
背景：拷贝数畸变（CNA）已被认为是转移性UM的主要特征。
目的：本研究旨在探索在UM分类中使用拷贝数变异（CNV）的可行性，预后分层和治疗反应。
方法：使用TCGA-UVM队列中的CNV数据对样品进行分类。通过“Limma”软件包筛选亚型之间的差异表达基因（DEGs）。通过对DEGs进行加权基因共表达网络分析(WGCNA)来鉴定与非整倍性评分相关的模块和中心基因。采用单变量Cox和最小绝对收缩和选择算子（LASSO）回归分析来训练枢纽基因，以开发UM的预后模型。最后,在UM细胞中验证了筛选的预后关键基因的表达水平，采用实时定量PCR(qRT-PCR)和transwell法检测细胞迁移和侵袭能力。
结果：UM样本分为3种CNV亚型，在总生存期（OS）和疾病特异性生存期（DSS）方面存在显着差异。C1的OS和DSS最短，免疫浸润水平最高。从这三种亚型共获得2036个DEG。通过WGCNA选择了与非整倍性评分最接近的80个hub基因。基于单变量Cox和LASSO回归分析最终确定8个基因为关键预后基因。包括HES6，RNASEH2C，NQO1、NUDT14、TTYH3、GJC1、FKBP10和MRPL24。使用这八个基因建立了一个预后模型，展示了强大的预测能力。不同风险组患者对免疫治疗的反应差异显著。我们发现高危患者对两种药物（Palbociclib_1054和Ribociclib_1632）更敏感，而低危患者对AZD1208_1449、ERK_2440_1713、Mirin_1048和Selumetinib_1736更敏感。
结论：本研究中的UM分为三种CNV亚型，并建立了基于8个非整倍体评分相关基因的模型来评估UM患者的预后和药物治疗效果。当前结果可能有助于UM管理的临床决策过程。
