Uveal melanoma (UM) is a subtype of melanoma with poor prognosis. This study aimed to construct a new prognostic gene signature that can be used for survival prediction and risk stratification of UM patients. In this work, transcriptome data from the Molecular Signatures Database were used to identify the cancer hallmarks most relevant to the prognosis of UM patients. Weighted gene co-expression network, univariate least absolute contraction and selection operator (LASSO), and multivariate Cox regression analyses were used to construct the prognostic gene characteristics. Kaplan–Meier and receiver operating characteristic (ROC) curves were used to evaluate the survival predictive ability of the gene signature. The results showed that glycolysis and immune response were the main risk factors for overall survival (OS) in UM patients. Using univariate Cox regression analysis, 238 candidates related to the prognosis of UM patients were identified (p < 0.05). Using LASSO and multivariate Cox regression analyses, a six-gene signature including ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3, and SLC39A4 was constructed. Kaplan–Meier analysis of the UM cohort in the training set showed that patients with higher risk scores had worse OS (HR = 2.61, p < 0.001). The time-dependent ROC (t-ROC) curve showed that the risk score had good predictive efficiency for UM patients in the training set (AUC > 0.9). Besides, t-ROC analysis showed that the predictive ability of risk scores was significantly higher than that of other clinicopathological characteristics. Univariate and multivariate Cox regression analyses showed that risk score was an independent risk factor for OS in UM patients. The prognostic value of risk scores was further verified in two external UM cohorts (GSE22138 and GSE84976). Two-factor survival analysis showed that UM patients with high hypoxia or immune response scores and high risk scores had the worst prognosis. Moreover, a nomogram based on the six-gene signature was established for clinical practice. In addition, risk scores were related to the immune infiltration profiles. Taken together, this study identified a new prognostic six-gene signature related to glycolysis and immune response. This six-gene signature can not only be used for survival prediction and risk stratification but also may be a potential therapeutic target for UM patients.

葡萄膜黑色素瘤（UM）是一种预后不良的黑色素瘤亚型。本研究旨在构建一种新的预后基因特征，可用于 UM 患者的生存预测和风险分层。在这项工作中，来自分子特征数据库的转录组数据用于识别与 UM 患者预后最相关的癌症标志。加权基因共表达网络、单变量最小绝对收缩和选择算子（LASSO）和多变量Cox回归分析用于构建预后基因特征。Kaplan-Meier 和受试者工作特征 (ROC) 曲线用于评估基因特征的生存预测能力。结果表明，糖酵解和免疫反应是 UM 患者总生存期 (OS) 的主要危险因素。p< 0.05)。使用 LASSO 和多变量 Cox 回归分析，六基因特征包括ARPC1B, BTBD6, 通用USB, KRTCAP2, RHBDD3， 和 SLC39A4被建造。对训练集中 UM 队列的 Kaplan-Meier 分析表明，风险评分较高的患者 OS 较差（HR = 2.61，p< 0.001)。时间依赖的 ROC（t-ROC）曲线表明，风险评分对训练集 UM 患者具有良好的预测效率（AUC > 0.9）。此外，t-ROC分析显示风险评分的预测能力显着高于其他临床病理特征。单变量和多变量 Cox 回归分析表明，风险评分是 UM 患者 OS 的独立危险因素。在两个外部 UM 队列（GSE22138 和 GSE84976）中进一步验证了风险评分的预后价值。双因素生存分析显示缺氧或免疫反应评分高、风险评分高的UM患者预后最差。此外，还为临床实践建立了基于六基因特征的列线图。此外，风险评分与免疫浸润谱相关。总之，这项研究确定了一种与糖酵解和免疫反应相关的新的预后六基因特征。这种六基因特征不仅可用于生存预测和风险分层，而且可能成为 UM 患者的潜在治疗靶点。