• metabonomics;
  • metabolic fingerprints;
  • multivariate statistical analysis;
  • biomarkers;
  • high performance liquid chromatography-diode array detector


The aim of this paper is to characterize metabolism disorders in Kunming mice induced by S180 and H22 tumor cells. Metabolic fingerprint based on high performance liquid chromatography-diode array detector (HPLC-DAD) was developed to map the disturbed metabolic responses. In vivo testing of the antitumor activity of paclitaxel (Taxol) was carried out by inhibiting the growth of S180 and H22 tumor cells. Based on 27 common peaks, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to distinguish the abnormal from control and to find significant endogenous compounds (SECs) which have significant contributions to classification. The tumor growth inhibition ratios (TIRs) of Taxol groups were used to validate the predictive accuracies of the PLS-DA models. The predictive accuracies of PLS-DA models for S180 and H22 tumor model groups were 97.6 and 100%, respectively. Nine (S180) and seven (H22) SECs were discovered, including uric acid and cytidine. In addition, the correlations between relative tumor weights (RTWs) and chromatographic data for the SECs were significant (p < 0.05). Investigations on the stability and precision of the established metabolic fingerprints demonstrate that the experiment is well controlled and reliable. This work shows that the platform of HPLC-DAD coupled with chemometric methods provides a promising method for the study of metabolism disorders induced by tumor cells. Copyright © 2011 John Wiley & Sons, Ltd.