Wu Jie, Wen Jun-Miao, Wang Yu-Chen, Luo Wen-Jie, Wang Qi-Feng, Lv Hong, Dai Bo, Ye Ding-Wei, Su Heng-Chuan, Zhu Yi-Ping
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Front Oncol. 2021 Mar 25;11:641385. doi: 10.3389/fonc.2021.641385. eCollection 2021.
This study aimed to assess the prognostic value of various diagnostic immunohistochemical (IHC) markers and develop an IHC-based classifier to predict the disease-free survival (DFS) of patients with bladder cancer undergoing radical cystectomy.
IHC was performed on tumor specimens from 366 patients with transitional cell bladder cancer. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to develop a multi-marker classifier for predicting DFS of patients with bladder cancer. The Kaplan-Meier estimate was performed to assess DFS, and unadjusted and adjusted Cox regression models were used to identify independent risk factors to predict DFS of patients with bladder cancer.
Based on the LASSO Cox regression model, nine prognostic markers were identified in the training cohort. Patients were stratified into low- and high-risk groups using the IHC-based classifier. In the training cohort, the 10-year DFS was significantly better in low-risk patients (71%) compared with high-risk patients (18%) (p < 0.001); in the validation cohort, the 10-year DFS was 86% for the low-risk group and 20% for the high-risk group (p < 0.001). Multivariable Cox regression analyses showed that the high-risk group based on the classifier was associated with poorer DFS adjusted by clinicopathological characteristics. Finally, a nomogram comprising the classifier and clinicopathological factors was developed for clinical application.
The nine-IHC-based classifier is a reliable prognostic tool, which can eventually guide clinical decision making regarding treatment strategy and follow-up scheduling of bladder cancer.
本研究旨在评估各种诊断性免疫组织化学（IHC）标志物的预后价值，并开发一种基于IHC的分类器，以预测接受根治性膀胱切除术的膀胱癌患者的无病生存期（DFS）。
对366例移行细胞膀胱癌患者的肿瘤标本进行IHC检测。采用最小绝对收缩和选择算子（LASSO）Cox回归模型开发用于预测膀胱癌患者DFS的多标志物分类器。采用Kaplan-Meier估计法评估DFS，并使用未调整和调整后的Cox回归模型确定预测膀胱癌患者DFS的独立危险因素。
基于LASSO Cox回归模型，在训练队列中确定了9个预后标志物。使用基于IHC的分类器将患者分为低风险组和高风险组。在训练队列中，低风险患者的10年DFS（71%）显著优于高风险患者（18%）（p<0.001）；在验证队列中，低风险组的10年DFS为86%，高风险组为20%（p<0.001）。多变量Cox回归分析表明，基于分类器的高风险组与经临床病理特征调整后的较差DFS相关。最后，开发了一个包含分类器和临床病理因素的列线图用于临床应用。
基于9种IHC的分类器是一种可靠的预后工具，最终可指导膀胱癌治疗策略和随访计划的临床决策。
