Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.
Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.
Breast Cancer Res Treat. 2019 May;175(1):59-75. doi: 10.1007/s10549-019-05147-6. Epub 2019 Feb 4.
To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.
A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.
A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXP) + (0.108 × EXP) + (0.105 × EXP) + (0.065 × EXP) + (- 0.126 × EXP) + (- 0.130 × EXP) + (0.116 × EXP) + (0.060 × EXP) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.
The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.
鉴定长链非编码 RNA（lncRNA）特征以预测乳腺癌（BRCA）患者的生存情况。
从 TCGA 数据库中下载了 1222 例 BRCA 病例和对照数据集。对差异表达的 mRNAs 进行加权基因共表达网络分析，生成与 BRCA 总生存状态相关的模块，并进一步构建竞争性内源性 RNA（ceRNA）网络的枢纽。使用单因素生存分析和多因素 Cox 风险模型分析生成用于预测 BRCA 患者生存的 lncRNA 特征，并使用接收者操作特征（ROC）曲线验证和特征化预测性能。
确定了由 8 个 lncRNA 特征组成的预后评分模型，预测评分=（0.121×EXP）+（0.108×EXP）+（0.105×EXP）+（0.065×EXP）+（-0.126×EXP）+（-0.130×EXP）+（0.116×EXP）+（0.060×EXP），中位评分 1.088。较高的评分预测风险较高。lncRNA 特征是与总生存相关的独立预后因素。该特征的 ROC 曲线下面积（AUC）分别为 0.979、0.844、0.99 和 0.997，逻辑回归、支持向量机、决策树和随机森林模型，在 TCGA 数据库的测试数据集中，预测 1 年至 10 年生存率的 AUC 值在 0.656 到 0.748 之间。
该 8-lncRNA 特征可作为预测 BRCA 总生存的独立生物标志物。lncRNA-miRNA-mRNA ceRNA 网络是识别与 BRCA 总生存相关的 lncRNA 的有效工具。
