尿沉渣PSGR检测在前列腺首次穿刺患者中的诊断作用

目的:探讨尿沉渣PSGR评分在前列腺癌患者中的诊断作用。方法:2010年1月~2013年10月收集299例在我院行前列腺穿刺患者前列腺按摩后尿液,应用实时定量PCR检测尿沉渣中PSGR mRNA和PSA mRNA的表达。PSGR评分使用PSGR mRNA/PSA mRNA×1 000计算;PSGR评分的预测作用使用ROC曲线分析并与血清PSA进行比较。结果:可分析样本比例为81.94%(245/299)。所有患者和PSA"灰区"患者前列腺穿刺阳性率分别为33.46%(83/245)和27.05%(33/122)。前列腺穿刺阳性患者PSGR评分明显高于前列腺穿刺阴性患者(P0.001)。经ROC曲线分析,在所有患者中,血清tPSA、PSGR评分和两者合用的曲线下面积分别为0.584、0.687和0.713。PSGR评分与血清总PSA诊断价值比较差异无统计学意义(P=0.052),但两者合用优于单用血清tPSA(P=0.002)。当处于PSA"灰区"时,血清tPSA、PSGR评分和两者合用的曲线下面积分别为0.525、0.727和0.731。PSGR评分的诊断价值明显优于血清tPSA(P=0.004)。结论:PSGR评分在前列腺穿刺患者中有良好的预测作用,血清tPSA和PSGR评分联合应用可提高预测总体患者的准确性,单用PSGR评分对PSA"灰区"患者有较好的预测作用。

Diagnostic power of PSGR score in urine sediments for detection prostate cancer in initial prostate biopsy

Objective ： To evaluate the diagnostic power of prostate-specific G-protein coupled receptor （PSGR） score in urine sediments to detect prostate cancer in initial prostate biopsy. Method.. Urine was collected after digital rectal examination （DRE） from 299 men before prostate biopsy. The specimens were collected from January 2010 to October 2013. The expression of PSGR mRNA and PSA mRNA were determined by quantitative real time polymerase chain reaction （qRT-PCR）. PSGR scores were calculated by PSGR mRNA/PSA mRNAX 1 000. The ability of PSGR score to predict the biopsy outcome was assessed with an AUC-ROC analysis and compared with the serum PSA levels. Result：The informative specimen rate was 81.94% （245/299）. The rates of positive prostate biopsy in overall patients and ＂PSA grey zone＂ patients were 33.46% （83/245） and 27.05% （33/122）. PSGR scores were significantly higher in patients with positive biopsy compared with patients of negative biopsy results （P〈0. 001）. In the group of overall patients, ROC curve analysis demonstrated that the area under ROC curve （AUC） of serum total PSA （tPSA）, PSGR score and the duplex model combining tPSA and PSGR score was 0. 584, 0. 687 and 0. 713, respectively. The duplex model represented a better approach to diagnosing PCa than tP- SA alone in prostate biopsy （P=0. 002）, but there is no statistical significance between tPSA and PSGR score （P =0. 052）. Further analysis of the diagnostic performance of PSGR score in the subgroup with PSA= 4-10 μg/L revealed that the area under ROC curve （AUC） of tPSA, PSGR score and the duplex model was 0. 525, 0. 727 and 0. 731, respectively. PSGR score was much better than tPSA alone to diagnose FCa in ＂PSA grey zone＂ subgroup （P= 0. 004）. Conclusion~PSGR is found to be a good predictor of prostate cancer in the initial prostate biopsy. The duplex model combining tPSA and PSGR score can improve the diagnostic potency in overall group and PSGR score alone can be a good predict