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前列腺肿瘤的临床可用RNA分析检测:效用与比较

Clinically available RNA profiling tests of prostate tumors: utility and comparison.

作者信息

Na Rong, Wu Yishuo, Ding Qiang, Xu Jianfeng

机构信息

Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai; Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.

Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China; Department of Urology, Huashan Hospital, Fudan University, Shanghai, China; Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA, .

出版信息

Asian J Androl. 2016 Jul-Aug;18(4):575-9. doi: 10.4103/1008-682X.175096.

Abstract

In the postscreening era, physicians are in need of methods to discriminate aggressive from nonaggressive prostate cancer (PCa) to reduce overdiagnosis and overtreatment. However, studies have shown that prognoses (e.g., progression and mortality) differ even among individuals with similar clinical and pathological characteristics. Existing risk classifiers (TMN grading system, Gleason score, etc.) are not accurately enough to represent the biological features of PCa. Using new genomic technologies, novel biomarkers and classifiers have been developed and shown to add value to clinical or pathological risk factors for predicting aggressive disease. Among them, RNA testing (gene expression analysis) is useful because it can not only reflect genetic variations but also reflect epigenetic regulations. Commercially available RNA profiling tests (Oncotype Dx, Prolaris, and Decipher) have demonstrated strong abilities to discriminate PCa with poor prognosis from less aggressive diseases. For instance, these RNA profiling tests can predict disease progression in active surveillance patients or early recurrence after radical treatments. These tests may offer more dependable methods for PCa prognosis prediction to make more accurate and personal medical decisions.

摘要

在筛查后时代,医生需要一些方法来区分侵袭性前列腺癌(PCa)和非侵袭性前列腺癌,以减少过度诊断和过度治疗。然而,研究表明,即使在具有相似临床和病理特征的个体中,预后(如进展和死亡率)也存在差异。现有的风险分类系统(TMN分级系统、 Gleason评分等)不足以准确代表PCa的生物学特征。利用新的基因组技术,已经开发出了新的生物标志物和分类系统,并显示出它们可为预测侵袭性疾病的临床或病理风险因素增添价值。其中,RNA检测(基因表达分析)很有用,因为它不仅可以反映基因变异,还可以反映表观遗传调控。市面上可买到的RNA分析检测(Oncotype Dx、Prolaris和Decipher)已显示出强大的能力,能够区分预后不良的PCa和侵袭性较低的疾病。例如,这些RNA分析检测可以预测接受主动监测的患者的疾病进展或根治性治疗后的早期复发。这些检测可能为PCa预后预测提供更可靠的方法,从而做出更准确的个性化医疗决策。

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