铜死亡相关长链非编码RNA可用于预测前列腺癌患者的生化复发

Cuproptosis-related IncRNAs to predict biochemical recurrence of prostate cancer

目的:构建一种铜死亡相关的长链非编码RNA(lncRNA)模型,为预测前列腺癌(PCa)患者的生化复发(BCR)提供新的思路和方法。方法:在肿瘤基因组图谱(TCGA)数据库中下载PCa患者的基因表达数据、突变负荷数据和临床资料数据,将临床数据随机分为训练组和验证组。筛选出铜死亡相关lncRNA,并构建由铜死亡相关lncRNA构成的预后风险评分模型。通过无生化复发(BCR-free)生存分析、Logistic回归分析以及独立预后分析等验证模型的有效性,并采用受试者工作特征曲线(ROC)分析、Kaplan-Meier生存曲线、相关性热图等对结果进行可视化。对肿瘤突变负荷(TMB)进行差异性分析和生存分析。最后,评估模型和TMB对PCa患者BCR的预测价值。结果:在训练组中筛选出6种铜死亡相关lncRNA用以构建预后风险评分模型,根据中位值将患者分为高低风险两组。在各组中随着风险评分的增加,发生BCR的患者数目也随之增加,且高风险组的BCR-free时间明显较短(P<0.05);在不同的年龄阶段该模型也表现出良好的鉴别效力(P<0.05);独立预后分析显示该模型是预测PCa患者BCR的可靠且独立的指标,优于其他临床病理学特征。TMB在高低风险组之间存在差异性表达(P<0.01)且与BCR显著相关。低风险评分、低TMB患者具有最高的BCR-free生存率(P<0.01)。结论:成功构建了一个铜死亡相关lncRNA模型,其可较为准确地预测PCa患者的BCR风险,预后风险评分越高患者出现BCR的可能性越大。TMB在高风险组患者中较高,TMB水平对于BCR有一定的提示意义。

Objective:To construct a cuproptosis-related IncRNA model and obtain some new ideas and methods for predicting the biochemical recurrence(BCR)of PCa.Methods:We identified cuproptosis-related lncRNAs from the gene expression data,mutation load data and clinical data on PCa patients in the Cancer Genome Atlas(TCGA)database and divided the patients into a training group and a verification group.We constructed a prognostic risk scoring model based on the cuproptosis-related IncRNAs,verified the validity of the model by BCR-free survival analysis,logistic regression analysis and independent prognosis analysis,and visualized the results using ROC curve analysis,Kaplan-Meier survival curves and the correlation heat map.We performed differential analysis and survival analysis of the tumor mutation burden(TMB),and assessed the value of the model and TMB in predicting the BCR of PCa.Results:A prognostic risk scoring model was successfully constructed based on the 6 cuproptosis-related IncRNAs identified from the PCa cases in the training group,which were divided into a high-and a low-risk groups according to the median value.The incidence of BCR rose with the increase of the risk score,and the BCR-free time was significantly shorter in the high-risk group(P<0.05).The model also exhibited a high differentiation value in different age groups(P<0.05),which was shown to be a reliable and independent prognostic indicator for predicting the BCR of PCa,even more valuable than other clinicopathological indicators.TMB was differentially expressed in the high-and low-risk groups(P<O.01)and significantly correlated with BCR.The highest rate of BCR-free survival was found in the patients with low risk scores and low TMB(P<O0.01).Conclusion:A cuproptosis-related IncRNA model was successfully constructed,which can accurately predict the risk of BCR in PCa patients.The higher the prognostic risk score,the greater the possibility of BCR.TMBis high in patients with a high risk,and the TMBlevel has certain suggestive significance for BCR.