Journal
METABOLITES
Volume 12, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/metabo12060550
Keywords
untargeted metabolomics; colorectal cancer; faecal samples; biomarkers
Categories
Funding
- Instituto de Salud Carlos (III) [DTS 19/00160]
- Basque Government [KK-2020/00008]
- European Regional Development Fund (FEDER) from the European Commission
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In this study, metabolomics analysis of fecal samples was conducted to identify significant metabolites in CRC and AA patients. A predictive model was established with a high accuracy of 91.67%.
Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Non-invasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753-0.9825), 0.7 and 1, respectively.
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