Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China; Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
EBioMedicine. 2018 May;31:54-65. doi: 10.1016/j.ebiom.2018.03.034. Epub 2018 Mar 31.
Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). Here, we report a genomic-clinicopathologic nomogram for preoperatively predicting LN metastasis in BCa. In the discovery stage, 325 BCa patients from TCGA were involved and LN-status-related mRNAs were selected. In the training stage, multivariate logistic regression analysis was used to developed a genomic-clinicopathologic nomogram for preoperative LN metastasis prediction in the training set (SYSMH set, n=178). In the validation stage, we validated the nomogram using two independent sample sets (SYSUCC set, n=142; RJH set, n=104) with respect to its discrimination, calibration and clinical usefulness. As results, we identified five LN-status-related mRNAs, including ADRA1D, COL10A1, DKK2, HIST2H3D and MMP11. Then, a genomic classifier was developed to classify patients into high- and low-risk groups in the training set. Furthermore, a nomogram incorporating the five-mRNA-based classifier, image-based LN status, transurethral resection (TUR) T stage, and TUR lymphovascular invasion (LVI) was constructed in the training set, which performed well in the training and validation sets. Decision curve analysis demonstrated the clinical value of our nomogram. Thus, our genomic-clinicopathologic nomogram shows favorable discriminatory ability and may aid in clinical decision-making, especially for cN-patients.
术前淋巴结 (LN) 状态对于膀胱癌 (BCa) 的治疗很重要。在这里，我们报告了一个用于术前预测膀胱癌 LN 转移的基因组-临床病理列线图。在发现阶段，我们纳入了来自 TCGA 的 325 名 BCa 患者，并选择了与 LN 状态相关的 mRNA。在训练阶段，我们使用多变量逻辑回归分析，在训练集（SYSMH 集，n=178）中建立了用于术前 LN 转移预测的基因组-临床病理列线图。在验证阶段，我们使用两个独立的样本集（SYSUCC 集，n=142；RJH 集，n=104）验证了该列线图在区分度、校准度和临床实用性方面的表现。结果，我们确定了五个与 LN 状态相关的 mRNA，包括 ADRA1D、COL10A1、DKK2、HIST2H3D 和 MMP11。然后，我们开发了一个基因组分类器，用于将患者在训练集中分为高风险和低风险组。此外，我们还在训练集中构建了一个包含基于五个 mRNA 的分类器、基于图像的 LN 状态、经尿道切除术 (TUR) T 分期和 TUR 脉管侵犯 (LVI) 的列线图，该列线图在训练集和验证集中表现良好。决策曲线分析表明了我们的列线图的临床价值。因此，我们的基因组-临床病理列线图显示出良好的区分能力，可能有助于临床决策，特别是对于 cN 患者。
