Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
National Clinical Research Center for Eye Diseases, Shanghai, China.
Transl Vis Sci Technol. 2022 May 2;11(5):9. doi: 10.1167/tvst.11.5.9.
Uveal melanoma (UM) is the most common primary malignant tumor with poor prognosis. The role of metabolism-related genes in the prognosis of UM remains unrevealed. This study aimed to establish and validate a prognostic prediction model for UM based on metabolism-related genes.
Gene expression profiles and clinicopathological information were downloaded from The Cancer Genome Atlas, and the Gene Expression Omnibus database. Univariable Cox regression, least absolute shrinkage and selection operator Cox regression, and stepwise regression were performed to establish the model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and calibration and discrimination analyses were used to evaluate the prognostic model.
Three metabolism-related genes, carbonic anhydrase 12, acyl-CoA synthetase long-chain family member 3, and synaptojanin 2, and three clinicopathological parameters (i.e., age, gender, and metastasis staging) were identified to establish the model. The risk score was found to be an independent prognostic factor for UM survival. High-risk patients demonstrated significantly poorer prognosis than low-risk patients. ROC analysis suggested the promising prognostic efficiency of the model. The calibration curve manifested satisfactory agreement between the predicted and observed risk. A nomogram and online survival calculator were developed to predict the survival probability.
The novel metabolism-based prognostic model could accurately predict the prognosis of UM patients, which facilitates the prediction of the survival probability by both ophthalmologists and patients with the online dynamic nomogram.
The dynamic nomogram links gene expression profiles to clinical prognosis of UM and is useful to evaluate the survival probability.
葡萄膜黑色素瘤（UM）是预后最差的最常见原发性恶性肿瘤。代谢相关基因在 UM 预后中的作用仍未被揭示。本研究旨在建立和验证基于代谢相关基因的 UM 预后预测模型。
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载基因表达谱和临床病理信息。采用单变量 Cox 回归、最小绝对收缩和选择算子 Cox 回归以及逐步回归来建立模型。Kaplan-Meier 生存分析、接收者操作特征（ROC）曲线分析以及校准和判别分析用于评估预后模型。
确定了 3 个代谢相关基因（碳酸酐酶 12、长链酰基辅酶 A 合成酶家族成员 3 和突触结合蛋白 2）和 3 个临床病理参数（年龄、性别和转移分期）来建立模型。风险评分被发现是 UM 生存的独立预后因素。高风险患者的预后明显比低风险患者差。ROC 分析表明该模型具有良好的预后效率。校准曲线表明预测风险与实际风险之间具有良好的一致性。建立了列线图和在线生存计算器以预测生存概率。
基于代谢的新型预后模型可以准确预测 UM 患者的预后，通过在线动态列线图，有助于眼科医生和患者预测生存概率。
翻译是否准确需要根据具体语境和专业知识来判断，以上译文仅供参考。
