Abstract
Bladder cancer is one of the most common urinary tract carcinomas in the world. Urine metabolomics is a promising approach for bladder cancer detection and marker discovery since urine is in direct contact with bladder epithelia cells; metabolites released from bladder cancer cells may be enriched in urine samples. In this study, we applied ultra-performance liquid chromatography time-of-flight mass spectrometry to profile metabolite profiles of 87 samples from bladder cancer patients and 65 samples from hernia patients. An OPLS-DA classification revealed that bladder cancer samples can be discriminated from hernia samples based on the profiles. A marker discovery pipeline selected six putative markers from the metabolomic profiles. An LLE clustering demonstrated the discriminative power of the chosen marker candidates. Two of the six markers were identified as imidazoleacetic acid whose relation to bladder cancer has certain degree of supporting evidence. A machine learning model, decision trees, was built based on the metabolomic profiles and the six marker candidates. The decision tree obtained an accuracy of 76.60%, a sensitivity of 71.88%, and a specificity of 86.67% from an independent test.
Highlights
Bladder cancer (BCa) is the ninth most common cancer in the world; 429,000 new cases and 165,000 deaths were estimated in 2012 [1]
The early stage BCa tumor denotes the superficial tumor without muscle involvement, while the advanced stage BCa tumor denotes the tumor invading to muscle layer
The testing set was used to evaluate the performance of the predictive model built using the training set; the testing set contained 47 subjects, including 32 BCa patients and 15 hernia patients
Summary
Bladder cancer (BCa) is the ninth most common cancer in the world; 429,000 new cases and 165,000 deaths were estimated in 2012 [1]. A 2016 official report of the Taiwan government said that in Taiwan there were 2,055 new cases of BCa (accounting for 2.07% of all cancers) and 833 deaths (1.86% of all cancers) in 2013 [3]. Cystoscopy and cytology are standards for BCa detection. Identifying discriminative markers for the noninvasive detection of BCa is essential. The sensitivity and specificity of these markers are not superior to existing detection methods, and the clinical utility of these markers has not been comprehensively examined [6,7,8,9]. There is a compelling need to develop more reliable BCa markers
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