Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring
Article
Zhu, J, Djukovic, D, Deng, L et al. (2021). Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring
. ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 407(26), 10.1007/s00216-015-8984-8
Zhu, J, Djukovic, D, Deng, L et al. (2021). Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring
. ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 407(26), 10.1007/s00216-015-8984-8
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.
