Liu Wentao, Qi Lin, Zu Xiongbin, Li Yuan, He Wei, Tong Shiyu, Chen Minfeng
Department of Urology, The second Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Urol Oncol. 2015 Apr;33(4):165.e9-14. doi: 10.1016/j.urolonc.2015.01.006. Epub 2015 Feb 13.
To assess the ability of a combined preoperative marker panel to identify patients with residual non-muscle-invasive bladder cancer who might benefit from repeat transurethral resection (reTUR).
Ki67, p53, vascular endothelial growth factor-C, E-cadherin, and survivin expressions were evaluated by immunohistochemical staining of surgical specimens from 72 patients who underwent reTUR. Related clinical and molecular markers were analyzed by univariate analyses to develop a marker panel. The predictive value of the marker panel was calculated by receiver operating characteristic curves.
Univariate analyses identified tumor size, number of tumors, p53 expression, E-cadherin expression, and the number of altered markers as risk factors for residual tumor (P = 0.03, 0.05, 0.06, 0.024, and 0.005, respectively). After adjusting for the effects of tumor stage and grade, multivariate analyses identified the number of altered markers as a risk factor for residual tumor (P = 0.004). The addition of tumor size, E-cadherin, and the number of altered markers to the base model (based on tumor stage and tumor grade) increased its discrimination for predicting residual tumor (5%, 6%, and 10%, respectively).
Some clinical and molecular markers could improve the accuracy of residual tumor prediction at reTUR. Such a marker panel may help to identify patients with non-muscle-invasive bladder cancer who have residual tumor after first TUR and who may therefore benefit from reTUR.
评估术前联合标志物组对识别可能从再次经尿道膀胱肿瘤切除术(reTUR)中获益的残留非肌层浸润性膀胱癌患者的能力。
通过对72例行reTUR患者的手术标本进行免疫组织化学染色,评估Ki67、p53、血管内皮生长因子-C、E-钙黏蛋白和生存素的表达。通过单因素分析相关临床和分子标志物以建立一个标志物组。通过受试者工作特征曲线计算标志物组的预测价值。
单因素分析确定肿瘤大小、肿瘤数量、p53表达、E-钙黏蛋白表达和改变的标志物数量为残留肿瘤的危险因素(P分别为0.03、0.05、0.06、0.024和0.005)。在调整肿瘤分期和分级的影响后,多因素分析确定改变的标志物数量为残留肿瘤的危险因素(P = 0.004)。在基础模型(基于肿瘤分期和肿瘤分级)中加入肿瘤大小、E-钙黏蛋白和改变的标志物数量可提高其预测残留肿瘤的辨别力(分别为5%、6%和10%)。
一些临床和分子标志物可提高reTUR时残留肿瘤预测的准确性。这样一个标志物组可能有助于识别首次TUR后有残留肿瘤且因此可能从reTUR中获益的非肌层浸润性膀胱癌患者。