Objective To establish early detection and diagnosis for bladder cancer.Methods In the current study,a metabolomics strategy was used to profile bladder cancer urine metabolites in mice and to further characterize the...Objective To establish early detection and diagnosis for bladder cancer.Methods In 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.Results Four 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.Conclusion These results suggest that combining chemometrics with the metabolites profile is an effective approach to aid in clinical diagnosis.展开更多
基金funding support from the Natural Science Foundation of China (No. 81673585 and No. 81603400)Hunan Provincial Key Laboratory of Diagnostics in Chinese Medicine Open Fund (No. 2015ZYZD13 and No. 2015ZYZD10)+2 种基金Key research and development project of Hunan Province Science and Technology (No. 2016SK2048)Innovative Project for Post-graduate of Hunan University of Chinese Medicine (No. 2017CX05)the National Standard Project of Chinese Medicine (No. ZYBZH-Y-HUN-21)
文摘Objective To establish early detection and diagnosis for bladder cancer.Methods In 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.Results Four 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.Conclusion These results suggest that combining chemometrics with the metabolites profile is an effective approach to aid in clinical diagnosis.
