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15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma
Disease Markers ( IF 3.464 ) Pub Date : 2020-12-01 , DOI: 10.1155/2020/9180732
Qiong Zhang 1 , Gang Ning 2 , Hongye Jiang 3 , Yanlin Huang 4 , Jinsong Piao 1 , Zhen Chen 3 , Xiaojun Tan 1 , Jiangyu Zhang 1 , Genglong Liu 1
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Background. Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. Methods. The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. Results. Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. Conclusion. Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

中文翻译:

基于 15-lncRNA 的分类器-临床病理学列线图提高了对肝细胞癌患者复发的预测

背景。我们的研究旨在开发基于 lncRNA 的分类器和包含基因组特征和临床病理因素的列线图,以帮助提高肝细胞癌 (HCC) 患者复发预测的准确性。方法. 从癌症基因组图谱(TCGA)下载了 374 名 HCC 患者和 50 名正常健康对照的 lncRNA 分析数据。使用单变量 Cox 回归和最小绝对收缩和选择算子 (LASSO) 分析,我们开发了基于 15-lncRNA 的分类器,并将我们的分类器与现有的 6-lncRNA 特征进行了比较。此外,还开发了包含基因组分类器和临床病理因素的列线图。基因组临床病理学列线图的预测准确性和判别能力由一致性指数 (C-index) 和校准曲线确定,并通过 C-index 和接受者操作特征 (ROC) 分析与 TNM 分期系统进行比较。进行决策曲线分析(DCA)以估计我们的列线图的临床价值。结果. 鉴定了 15 个与无复发生存 (RFS-) 相关的 lncRNA,由鉴定出的 15 个 lncRNA 组成的分类器可以有效地将患者分为高风险和低风险亚组。基于 15-lncRNA 的分类器预测 2 年和 5 年 RFS 的预测准确率在训练集和验证集分别为 0.791 和 0.834,在验证集中分别为 0.684 和 0.747,优于现有的 6-lncRNA 特征. 此外,基因组临床病理学列线图预测RFS的AUC在训练集为0.837,在验证集为0.753,基因组临床病理学列线图的C指数在训练集为0.78(0.72-0.83),在训练集为0.71( 0.65-0.76)在验证集中,优于传统的 TNM 阶段和基于 15-lncRNA 的分类器。决策曲线分析进一步表明,我们的列线图比 TNM 阶段和基于 15-lncRNA 的分类器具有更大的净收益。结果得到了外部证实。结论。与 TNM 阶段相比,基于 15-lncRNAs 的分类器临床病理学列线图是识别 HCC 复发的更有效和有价值的工具,可能有助于临床决策。
更新日期:2020-12-01
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