Shen Chong, Sun Zeyu, Chen Deying, Su Xiaoling, Jiang Jing, Li Gonghui, Lin Biaoyang, Yan Jiajun
1 Department of Urology, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University, the First Affiliated Hospital of Shaoxing University), Shaoxing, People's Republic of China .
OMICS. 2015 Jan;19(1):1-11. doi: 10.1089/omi.2014.0116.
Early detection is vital to improve the overall survival rate of bladder cancer (BCa) patients, yet there is a lack of a reliable urine-based assay for early detection of BCa. Urine metabolites represented a potential rich source of biomarkers for BCa. This study aimed to develop a metabolomics approach for high coverage discovery and identification of metabolites in urine samples. Urine samples from 23 early stage BCa patients and 21 healthy volunteers with minimum sample preparations were analyzed by a short 30 min UPLC-HRMS method. We detected and quantified over 9000 unique UPLC-HRMS features, which is more than four times than about 2000 features detected in previous urine metabolomic studies. Furthermore, multivariate OPLS-DA classification models were established to differentiate urine samples from bladder cancer cohort and normal health cohort. We identified three BCa-upregulated metabolites: nicotinuric acid, trehalose, AspAspGlyTrp, and three BCa-downregulated metabolites: inosinic acid, ureidosuccinic acid, GlyCysAlaLys. Finally, analysis of six post-surgery BCa urine samples showed that these BCa-metabolomic features reverted to normal state after tumor removal, suggesting that they reflected metabolomic features associated with BCa. ROC analyses using two linear regression models to combine the identified markers showed a high diagnostic performance for detecting BCa with AUC (area under the ROC curve) values of 0.919 to 0.934. In summary, we developed a high coverage metabolomic approach that has potential for biomarker discovery in cancers.
早期检测对于提高膀胱癌（BCa）患者的总体生存率至关重要，但目前缺乏一种可靠的基于尿液的BCa早期检测方法。尿液代谢物是BCa生物标志物的一个潜在丰富来源。本研究旨在开发一种代谢组学方法，用于高覆盖率地发现和鉴定尿液样本中的代谢物。采用短时间30分钟的超高效液相色谱-高分辨率质谱（UPLC-HRMS）方法，对23例早期BCa患者和21名健康志愿者的尿液样本进行了分析，样本制备最少。我们检测并定量了9000多个独特的UPLC-HRMS特征，这比之前尿液代谢组学研究中检测到的约2000个特征多出四倍多。此外，还建立了多变量正交投影判别分析（OPLS-DA）分类模型，以区分膀胱癌队列和正常健康队列的尿液样本。我们鉴定出三种BCa上调代谢物：烟尿酸、海藻糖、天冬氨酰天冬氨酰甘氨酰色氨酸，以及三种BCa下调代谢物：肌苷酸、脲基琥珀酸、甘氨酰半胱氨酰丙氨酰赖氨酸。最后，对六例术后BCa尿液样本的分析表明，这些BCa代谢组学特征在肿瘤切除后恢复到正常状态，这表明它们反映了与BCa相关的代谢组学特征。使用两个线性回归模型组合鉴定出的标志物进行的ROC分析显示，检测BCa具有较高的诊断性能，ROC曲线下面积（AUC）值为0.919至0.934。总之，我们开发了一种高覆盖率的代谢组学方法，具有在癌症中发现生物标志物的潜力。
