【摘要】 目的 应用生物信息学构建与铁死亡基因相关的UVM预测模型，用于评估葡萄膜黑色素瘤患者预后生存及转移情况。方法 从肿瘤基因组图谱(TCGA)获取UVM转录组数据和与之匹配的临床信息，结合铁死亡数据库确定铁死亡相关基因。Kaplan-Meier方法分析铁死亡相关基因在高、低表达状态下患者的总体生存率，利用LASSO、单因素和多因素回归分析筛选并构建免疫相关风险预测模型。根据风险评分将患者分为高、低风险组，利用ROC曲线评估模型准确度，并采用基因表达综合数据库(GEO)中的GSE84976和GSE22138作为验证集。结果 通过多种算法构建了含有4个核心基因的预测模型(AIFM2、ITGA6、CD44、ALOX12),基于模型标志物计算的风险评分可以准确识别UVM高、低风险患者，并具有预测患者总体生存的能力(P<0.01,1 a AUC=0.84、3 a AUC=0.89、5 a AUC=0.91),验证集GSE84976外部验证结果：P<0.01,1 a AUC=NA,3 a AUC=0.78,5 a AUC=0.91;验证集GSE22138外部验证结果：P<0.01,1 a AUC=0.75,3 a AUC=0.79,5 a AUC=0.82,结果显示，该预测模型可作为UVM独立预后因素，预测生存及转移情况。结论 构建的新型铁死亡相关的UVM预测模型(AIFM2、ITGA6、CD44、ALOX12),能够准确预测UVM患者预后及转移情况，可为UVM患者的个性化诊疗提供新的靶点。更多还原

【Abstract】 Objective Application of bioinformatics to construct UVM prediction model related to ferroptosis genes for evaluating prognosis, survival and metastasis of patients.Method Obtain UVM transcriptome data and matched clinical information from The Cancer Genome Atlas(TCGA),and combine it with the ferroptosis database to identify ferroptosis related genes.Kaplan-Meier method was used to analyze the overall survival rate of patients with ferroptosis related genes in high and low expression states.LASSO,univariate and multivariate regression analysis were used to screen and construct ferroptosis related risk prediction models.According to the risk score, patients are divided into high and low risk groups, and the accuracy of the model is evaluated by using the ROC curve.GSE84976 and GSE22138 from the Gene Expression Omnibus(GEO) was used as validation.Results A prediction model containing four core genes(AIFM2,ITGA6,CD44,ALOX12) was constructed through multiple algorithms.The risk score based on model markers can accurately identify high/low risk patients with UVM and have the ability to predict overall survival of patients(P<0.01,1-year AUC=0.84,3-year AUC=0.89,5-year AUC=0.91).The validation set GSE84976 external validation results: P<0.01,1-year AUC=NA,3-year AUC=0.78,5-year AUC=0.91;The validation set GSE22138 external validation results: P<0.01,1-year AUC=0.75,3-year AUC=0.79,5-year AUC=0.82,the result display that this prediction model can serve as an independent prognostic factor to predict survival and metastasis of UVM.Conclusion This study constructed a novel UVM prediction model related to ferroptosis(AIFM2,ITGA6,CD44,ALOX12),accurately predicted the prognosis and metastasis of UVM patients, and will provide new targets for personalized diagnosis and treatment of UVM patients.更多还原