Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.
2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.
Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy (1H-NMR), a potential non-invasive early tumor detection method, was used to profile urine metabolites from 55 CRC patients and 40 healthy controls (HCs). Pattern recognition through orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to 1H-NMR processed data. Model specificity was confirmed by comparison with esophageal cancers (EC, n=18). Unique metabolomic profiles distinguished all CRC stages from HC urine samples. A total of 16 potential biomarker metabolites were identified in stage I/II CRC, indicating amino acid metabolism, glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, choline metabolism, and gut microflora metabolism pathway disruptions. Metabolite profiles from early stage CRC and EC patients were also clearly distinguishable, suggesting that upper and lower gastrointestinal cancers have different metabolomic profiles. Our study assessed important metabolomic variations in CRC patient urine samples, provided information complementary to that collected from other biofluid-based metabolomics analyses, and elucidated potential underlying metabolic mechanisms driving CRC. Our results support the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC.
Previous studies have compared fecal metabolites from healthy and colorectal cancer (CRC) patients to predict the pro-CRC signatures. However, the systemic mechanistic link between feces and colonic tissues of CRC patients is still limited. The current study was a paralleled investigation of colonic tumor tissues and their normal adjacent tissues alongside patient-matched feces by using 1 H nuclear magnetic resonance spectroscopy combined with pattern recognition to investigate how fecal metabolic phenotypes are linked to the changes in colorectal tumor profiles. A set of overlapping discriminatory metabolites across feces and tumor tissues of CRC were identified, including elevated levels of lactate, glutamate, alanine, succinate and reduced amounts of butyrate. These changes could indicate the networks for metabolic pathway perturbations in CRC potentially involved in the disruptions of glucose and glycolytic metabolism, TCA cycle, glutaminolysis, and short chain fatty acids metabolism. Furthermore, changes in fecal acetate were positively correlated with alterations of glucose and myo-inositol in colorectal tumor tissues, implying enhanced energy production for rapid cell proliferation. Compared to other fecal metabolites, acetate demonstrated the highest diagnostic performance for diagnosing CRC, with an AUC of 0.843 in the training set, and a good predictive ability in the validation set. Overall, these associations provide evidence of distinct metabolic signatures and metabolic pathway disturbances between the colonic tissues and feces within the same individual, and changes of fecal metabolic signature could reflect the CRC tissue microenvironment, highlighting the significance of the distinct fecal metabolic profiles as potential novel and noninvasive relevant indicators for CRC detection.
BACKGROUND Several studies have demonstrated a correlation between esophageal cancer (EC) and perturbed urinary metabolomic profiles, but none has described the correlation between urine metabolite profiles and those of the tumor and adjacent esophageal mucosa in the same patient. AIM To investigate how urinary metabolic phenotypes were linked to the changes in the biochemical landscape of esophageal tumors. METHODS Nuclear magnetic resonance-based metabolomics were applied to esophageal tumor tissues and adjacent normal mucosal tissues alongside patient-matched urine samples. RESULTS Analysis revealed that specific metabolite changes overlapped across both metrics, including glucose, glutamate, citrate, glycine, creatinine and taurine, indicating that the networks for metabolic pathway perturbations in EC, potentially involved in but not limited to disruption of fatty acid metabolism, glucose and glycolytic metabolism, tricarboxylic acid cycle and glutaminolysis. Additionally, changes in most urinary biomarkers correlated with changes in biomarker candidates in EC tissues, implying enhanced energy production for rapid cell proliferation. CONCLUSION Overall, these associations provide evidence for distinct metabolic signatures and pathway disturbances between the tumor tissues and urine of EC patients, and changes in urinary metabolic signature could reflect reprogramming of the aforementioned metabolic pathways in EC tissues. Further investigation is needed to validate these initial findings using larger samples and to establish the underlying mechanism of EC progression.
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