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Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring

机译:针对大肠癌进展监测的靶向血清代谢物谱分析和顺序代谢物比率分析

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Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.
机译:大肠癌(CRC)是世界上最流行的癌症之一,也是人类发病率和死亡率的主要原因。除了及早发现外,密切监测CRC中疾病的进展对于患者的预后和治疗决策至关重要。已努力开发新的方法以改善早期检测和患者监测。然而,有关使用代谢组学研究CRC监测治疗反应和疾病复发的研究尚未报道。在此概念验证研究中,我们应用了靶向液相色谱串联质谱(LC-MS / MS)代谢谱分析方法,该方法侧重于系列血清样品的顺序代谢物比率分析,以监测20名CRC患者的疾病进展。连续样品的使用减少了患者之间的代谢变异性。建立了使用五个代谢物(琥珀酸酯,N2,N2-二甲基鸟苷,腺嘌呤,柠康酸和1-甲基鸟苷)组成的偏最小二乘判别分析(PLS-DA)模型,并获得了出色的模型性能(灵敏度= 0.83,特异性= 0.94,获得的受体操作者特征曲线下面积(AUROC)= 0.91,优于传统的CRC监测标志物癌胚抗原(灵敏度= 0.75,特异性= 0.76,AUROC = 0.80),并采用了蒙特卡洛交叉验证,通过将真实分类模型与随机排列模型分离,可以清楚地观察到我们模型的鲁棒性,我们的研究结果表明代谢谱分析在CRC疾病监测中具有潜在的实用性。

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