Predicting disease progression after nephrectomy for localized renal cell carcinoma: the utility of prognostic models and molecular biomarkers.
Predicting disease progression after nephrectomy for localized renal cell carcinoma: the utility of prognostic models and molecular biomarkers.
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作者：
Crispen PL;Boorjian SA;Lohse CM;Leibovich BC;Kwon ED
鉴于现有治疗方案对转移性疾病的疗效有限，病理性局限性肾细胞癌（RCC）切除后的疾病进展与显著的死亡率相关。因此，一些辅助试验被设计用于治疗术后RCC进展风险特别高的患者。目前有几种不同的预后模型，用于识别疾病进展高风险的患者。虽然这些可用的预测模型提供了对患者疾病进展风险的合理评估，但这些模型的准确性可以通过结合分子预后生物标志物进一步提高。虽然已经描述了许多候选分子，但很少有专门评估与肾切除术后疾病进展的关系。IMP-3、CXCR3、p53、Survivin、cIAP1、B7-H1和B7-H4均与肾切除术后疾病进展相关。这些生物标志物中的一种或几种的结合可以提高目前可用的预后模型的准确性，从而促进旨在预防未来疾病进展的辅助治疗的适当使用。因此，作者回顾了目前用于预测局部RCC疾病进展的预后工具，并详细介绍了迄今为止在这种情况下评估各种生物标志物的研究。
Disease progression after nephrectomy for pathologically localized renal cell carcinoma (RCC) is associated with a significant mortality rate, given the limited efficacy of available treatment regimens for metastatic disease. As such, several adjuvant trials have been designed to treat patients at particularly high risk for post-surgical RCC progression. Several different prognostic models designed to identify patients at high risk of disease progression are available. Although these available predictive models provide a reasonable assessment of patients’ risks of disease progression, the accuracy of these models may further be improved via the incorporation of molecular prognostic biomarkers. Although numerous candidate molecules have been described, few have been specifically assessed for the association with disease progression after nephrectomy. IMP-3, CXCR3, p53, Survivin, cIAP1, B7-H1, and B7-H4 have all been associated with disease progression after nephrectomy. The incorporation of 1 or several of these biomarkers may increase the accuracy of currently available prognostic models and thereby facilitate the appropriate use of adjuvant therapies aimed at preventing future disease progression. As such, the authors review the current prognostic tools for predicting disease progression for localized RCC, and detail studies to date that have evaluated various biomarkers in this setting.
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影响因子：
51.1
作者：
Jiang, Zhong;Chu, Peigou G.;Wu, Chin-Lee
通讯作者：
Wu, Chin-Lee
影响因子：
6.2
作者：
Cindolo, L;Patard, JJ;Gallo, C
通讯作者：
Gallo, C
DOI：
10.1016/j.urolonc.2007.05.009
发表时间：
2007-09-01
影响因子：
2.7
作者：
Haas, Naomi B.;Uzzo, Robert G.
通讯作者：
Uzzo, Robert G.
影响因子：
6.2
作者：
Inman, Brant A.;Sebo, Thomas J.;Kwon, Eugene D.
通讯作者：
Kwon, Eugene D.
影响因子：
45.3
作者：
Eggener, Scott E.;Yossepowitch, Ofer;Russo, Paul
通讯作者：
Russo, Paul