Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method
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Yang Zhou1,2,*, Ruixiang Song3,*, Chong Ma3,*, Lina Zhou1, Xinyu Liu1,2, Peiyuan Yin1, Zhensheng Zhang3, Yinghao Sun3, Chuanliang Xu3, Xin Lu1, Guowang Xu1
1Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Department of Urology, Shanghai Changhai Hospital, Secondary Military Medical University, Shanghai 200433, China
*These authors contributed equally to this work
Xin Lu, email: [email protected]
Chuanliang Xu, email: [email protected]
Keywords: metabolomics, gas chromatography-mass spectrometry, biomarker, urine, bladder cancer
Received: November 15, 2016 Accepted: January 24, 2017 Published: February 01, 2017
Bladder cancer (BC) is the second most prevalent malignancy in the urinary system and is associated with significant mortality; thus, there is an urgent need for novel noninvasive diagnostic biomarkers. A urinary pseudotargeted method based on gas chromatography–mass spectrometry was developed and validated for a BC metabolomics study. The method exhibited good repeatability, intraday and interday precision, linearity and metabolome coverage. A total of 76 differential metabolites were defined in the discovery sample set, 58 of which were verified using an independent validation urine set. The verified differential metabolites revealed that energy metabolism, anabolic metabolism and cell redox states were disordered in BC. Based on a binary logistic regression analysis, a four-biomarker panel was defined for the diagnosis of BC. The area under the receiving operator characteristic curve was 0.885 with 88.0% sensitivity and 85.7% specificity in the discovery set and 0.804 with 78.0% sensitivity and 70.3% specificity in the validation set. The combinatorial biomarker panel was also useful for the early diagnosis of BC. This approach can be used to discriminate non-muscle invasive and low-grade BCs from healthy controls with satisfactory sensitivity and specificity. The results show that the developed urinary metabolomics method can be employed to effectively screen noninvasive biomarkers.
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