Chemometrics-aided Metabolic Fingerprint Method Applied in Bladder Cancer Stages Differentiating

ObjectiveTo establish early detection and diagnosis for bladder cancer.

MethodsIn the current study, a metabolomics strategy was used to profile bladder cancer urine metabolites in mice and to further characterize the disease status at different stages. In addition, some chemometrics algorithms were adopted to analyze the metabolites fingerprints, including baseline removal and retention time shift, to overcome variations in the experimental process. After processing, metabolites were qualitatively and quantitatively analyzed in each sample at different stages. Finally, a random forest algorithm was used to discriminate the differences among different groups.

ResultsFour potential biomarkers, including glyceric acid, (R^*, R^*)-2, 3-Dihydroxybutanoic acid, N-(1-oxohexyl)-glycine and D-Turanose, were discovered by exploring the characteristics of different groups.

ConclusionThese results suggest that combining chemometrics with the metabolites profile is an effective approach to aid in clinical diagnosis.