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A four-methylated LncRNA signature predicts survival of osteosarcoma patients based on machine learning
Genomics ( IF 3.4 ) Pub Date : 2020-10-15 , DOI: 10.1016/j.ygeno.2020.10.010
Yajun Deng 1 , Wenhua Yuan 2 , Enhui Ren 3 , Zuolong Wu 3 , Guangzhi Zhang 3 , Qiqi Xie 1
Affiliation  

Risk stratification using prognostic markers facilitates clinical decision-making in treatment of osteosarcoma (OS). In this study, we performed a comprehensive analysis of DNA methylation and transcriptome data from OS patients to establish an optimal methylated lncRNA signature for determining OS patient prognosis. The original OS datasets were downloaded from the the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Univariate, Lasso, and machine learning algorithm-iterative Lasso Cox regression analyses were used to establish a methylated lncRNA signature that significantly correlated with OS patient survival. The validity of this signature was verified by the Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves. We established a four-methylated lncRNA signature that can predict OS patient survival (verified in independent cohort [GSE39055]). Kaplan-Meier analysis showed that the signature can distinguish between the survival of high- and low-risk patients. ROC analysis corroborated this finding and revealed that the signature had higher prediction accuracy than known biomarkers. Kaplan-Meier analysis of the clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. The four-methylated lncRNA signature is an independent prognostic biomarker of OS.



中文翻译:

基于机器学习的四甲基化 LncRNA 特征预测骨肉瘤患者的生存

使用预后标志物进行风险分层有助于骨肉瘤 (OS) 治疗的临床决策。在这项研究中,我们对 OS 患者的 DNA 甲基化和转录组数据进行了全面分析,以建立用于确定 OS 患者预后的最佳甲基化 lncRNA 特征。原始 OS 数据集是从 Therapeutical Applicable Research to Generate Effective Treatments (TARGET) 数据库下载的。单变量、Lasso 和机器学习算法迭代 Lasso Cox 回归分析用于建立与 OS 患者存活显着相关的甲基化 lncRNA 特征。该签名的有效性通过 Kaplan-Meier 曲线、接受者操作特征 (ROC) 曲线进行了验证。我们建立了一个四甲基化 lncRNA 特征,可以预测 OS 患者的存活率(在独立队列 [GSE39055] 中得到验证)。Kaplan-Meier 分析表明,该特征可以区分高危和低危患者的生存情况。ROC 分析证实了这一发现,并显示该特征具有比已知生物标志物更高的预测准确性。临床亚组的 Kaplan-Meier 分析表明,该特征的预后能力与临床病理因素无关。四甲基化 lncRNA 特征是 OS 的独立预后生物标志物。ROC 分析证实了这一发现,并显示该特征具有比已知生物标志物更高的预测准确性。临床亚组的 Kaplan-Meier 分析表明,该特征的预后能力与临床病理因素无关。四甲基化 lncRNA 特征是 OS 的独立预后生物标志物。ROC 分析证实了这一发现,并显示该特征具有比已知生物标志物更高的预测准确性。临床亚组的 Kaplan-Meier 分析表明,该特征的预后能力与临床病理因素无关。四甲基化 lncRNA 特征是 OS 的独立预后生物标志物。

更新日期:2020-10-16
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