Low- and High-Grade Bladder Cancer Determination via Human Serum-Based Metabolomics Approach
ABSTRACT To address the shortcomings of urine cytology and cystoscopy for probing and grading of urinary bladder cancer (BC), we applied 1H-nuclear magnetic resonance (NMR) spectroscopy as a surrogate method for identification of BC. This study includes 99 serum samples; comprising low-grade (LG, n=36) and high-grade (HG, n=31) of BC, and healthy controls (HC, n=32). 1H NMR-derived serum data were analyzed using orthogonal partial least-squares discriminant analysis (OPLS-DA). OPLS-DA-derived model validity was confirmed using an internal and external cross-validation. Internal validation was performed using initial samples (n=99) data set. External validation was performed on new batch of suspected BC patients (n=106) through double blind study. Receiver operating characteristic (ROC) curve analysis was also performed. OPLS-DA-derived serum metabolomics (6 biomarkers, ROC; 0.99) were able to discriminate 95% of BC cases with 96% sensitivity and 94% specificity when compared to HC. Likewise (3 biomarkers, ROC; 0.99), 98% of cases of LG were able to differentiate from HG with 97% sensitivity and 99% specificity. External validation reveals comparable results to internal validation. 1H NMR-based serum metabolic screening appears to be a promising and least-invasive approach for probing and grading of BC in contrast to the highly invasive and painful cystoscopic approach of BC detection.
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ABSTRACT: Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical and therapeutic clinical trials. In this review, introduction to metabonomics, typical metabonomic workflow and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.Journal of Proteome Research 11/2014; 14(2). DOI:10.1021/pr500966h · 5.00 Impact Factor
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ABSTRACT: Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling LC-MS, to provide long-term analytical reproducibility and facilitate metabolome comparison among different datasets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis, and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection, and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistics analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery dataset and 0.935 in the verification dataset. These results demonstrated the utility of the UMS method for long-term metabolomics and discovering potential metabolite biomarkers for diagnosis of bladder cancer.Analytical Chemistry 05/2014; 86(13). DOI:10.1021/ac5011684 · 5.83 Impact Factor