Abstract
Effective diagnosis and surveillance of bladder cancer (BCa) is currently challenged by detection methods that are of poor sensitivity, particularly for low-grade tumors, resulting in unnecessary invasive procedures and economic burden. We performed HR-MAS NMR-based global metabolomic profiling and applied unsupervised principal component analysis (PCA) and hierarchical clustering performed on NMR data set of bladder-derived tissues and identified metabolic signatures that differentiate BCa from benign disease. A partial least-squares discriminant analysis (PLS-DA) model (leave-one-out cross-validation) was used as a diagnostic model to distinguish benign and BCa tissues. Receiver operating characteristic curve generated either from PC1 loadings of PCA or from predicted Y-values resulted in an area under curve of 0.97. Relative quantification of more than 15 tissue metabolites derived from HR-MAS NMR showed significant differences (P < 0.001) between benign and BCa samples. Noticeably, striking metabolic signatures were observed even for early stage BCa tissues (Ta-T1), demonstrating the sensitivity in detecting BCa. With the goal of cross-validating metabolic signatures derived from HR-MAS NMR, we utilized the same tissue samples to analyze 8 metabolites through gas chromatography-mass spectrometry (GC-MS)-targeted analysis, which undoubtedly complements HR-MAS NMR-derived metabolomic information. Cross-validation through GC-MS clearly demonstrates the utility of a straightforward, nondestructive, and rapid HR-MAS NMR technique for clinical diagnosis of BCa with even greater sensitivity. In addition to its utility as a diagnostic tool, these studies will lead to a better understanding of aberrant metabolic pathways in cancer as well as the design and implementation of personalized cancer therapy through metabolic modulation.