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

化学计量学结合代谢指纹图谱用于不同膀胱癌分期的区分（英文）

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.

目的建立膀胱癌早期发现与诊断方法。方法采用代谢组学策略来分析膀胱癌小鼠的尿液代谢物,进一步阐述疾病在不同阶段的状态。此外,采用化学计量学处理代谢指纹图谱,包括基线去除和保留时间迁移,以克服实验过程中的变化。继而,对不同阶段的每个样品进行定性和定量分析。最后,采用随机森林算法对处理后的代谢指纹图谱进行分析,区别不同膀胱癌分期之间的差异。结果为了探索不同膀胱癌分期的特征,本研究发现了四种潜在的生物标志物,包括甘油酸、(R*,R*)-2,3-二羟基丁酸、N-(1-氧代己基)-甘氨酸和D-谷氨糖。结论将化学计量学与代谢组学相结合可有效辅助膀胱癌的临床诊断。