ORIGINAL ARTICLE Urine metabolite profiling offers potential early diagnosis of oral cancer Guo X. Xie • Tian L. Chen • Yun P. Qiu • Peng Shi • Xiao J. Zheng • Ming M. Su • Ai H. Zhao • Zeng T. Zhou • Wei Jia Received: 13 January 2011 / Accepted: 18 March 2011 Ó Springer Science+Business Media, LLC 2011 Abstract Oral cancer is the sixth most common human cancer, with a high morbidity rate and an overall 5-year survival rate of less than 50%. It is often not diagnosed until it has reached an advanced stage. Therefore, an early diagnostic and stratification strategy is of great importance for oral cancer. In the current study, urine samples of patients with oral squamous cell carcinoma (OSCC, n = 37), oral leukoplakia (OLK, n = 32) and healthy subjects (n = 34) were analyzed by gas chromatography- mass spectrometry (GC–MS). Using multivariate statistical analysis, the urinary metabolite profiles of OSCC, OLK and healthy control samples can be clearly discriminated and a panel of differentially expressed metabolites was obtained. Metabolites, valine and 6-hydroxynicotic acid, in combination yielded an accuracy of 98.9%, sensitivity of 94.4%, specificity of 91.4%, and positive predictive value of 91.9% in distinguishing OSCC from the controls. The combination of three differential metabolites, 6-hydroxy- nicotic acid, cysteine, and tyrosine, was able to discrimi- nate between OSCC and OLK with an accuracy of 92.7%, sensitivity of 85.0%, specificity of 89.7%, and positive predictive value of 91.9%. This study demonstrated that the metabolite markers derived from this urinary metabolite profiling approach may hold promise as a diagnostic tool for early stage OSCC and its differentiation from other oral conditions. Keywords Oral cancer Á Metabonomics Á Metabolomics Á Oral squamous cell carcinoma Á Oral leukoplakia Á Urine Á Gas chromatography–mass spectrometry Á Multivariate statistical analysis Á Receiver operating characteristic Abbreviations OSCC Oral squamous cell carcinoma OLK Oral leukoplakia GC–MS Gas chromatography–mass spectrometry PCA Principal component analysis OPLS-DA Orthogonal partial least squares-discriminant analysis VIP Variable importance in the projection ROC Receiver operating characteristic LR Logistic regression FDR False discovery rate R2X Fraction of sum of squares (SS) of X explained by each component R2Y Fraction of sum of squares (SS) of Y explained by each component Electronic supplementary material The online version of this article (doi:10.1007/s11306-011-0302-7) contains supplementary material, which is available to authorized users. G. X. Xie Á X. J. Zheng Á M. M. Su Á A. H. Zhao School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China G. X. Xie Á Y. P. Qiu Á W. Jia (&) Department of Nutrition, The University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA e-mail: w_jia@uncg.edu T. L. Chen Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for System Biomedicine and School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China P. Shi Á Z. T. Zhou (&) Department of Oral Mucosal Diseases, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China e-mail: zhouzengtong@hotmail.com 123 Metabolomics DOI 10.1007/s11306-011-0302-7