Developing Urinary Metabolomic Signatures as Early Bladder Cancer Diagnostic Markers
Developing Urinary Metabolomic Signatures as Early Bladder Cancer Diagnostic Markers
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开发尿液代谢组学特征作为早期膀胱癌诊断标志物
DOI：
10.1089/omi.2014.0116
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发表时间：
2015-01-01
影响因子：
3.3
通讯作者：
Yan, Jiajun
中科院分区：
文献类型：
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作者：
Shen, Chong;Sun, Zeyu;Yan, Jiajun
早期检测对提高膀胱癌(BCA)患者的总体生存率至关重要，但目前还缺乏可靠的尿液早期检测方法。尿代谢物是BCA生物标志物的潜在丰富来源。本研究旨在开发一种高覆盖率的代谢组学方法来发现和鉴定尿样中的代谢物。对23例早期BCA患者和21例健康志愿者的尿样进行了短时30min的UPLC-HRMS分析。我们检测并量化了9000多个独特的UPLC-HRMS特征，这是先前尿液代谢研究中检测到的约2000个特征的四倍多。在此基础上，建立了尿样与膀胱癌队列和正常健康队列的多变量OPLS-DA分类模型。我们鉴定了三种BCA上调的代谢物：烟酸、海藻糖、AspAspGlyTrp和三种BCA下调的代谢物：肌苷酸、尿素琥珀酸、GlyCysAlaLys。最后，对6个BCA术后尿样的分析表明，这些BCA代谢特征在肿瘤切除后恢复到正常状态，表明它们反映了与BCA相关的代谢特征。ROC分析使用两个线性回归模型将识别的标志物组合在一起，显示出对于AUC值在0.919到0.934之间的胆囊癌的高诊断性能。总之，我们开发了一种高覆盖率的代谢组学方法，有可能在癌症中发现生物标记物。
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.