The online version of this article (doi:10.1186/1477-7819-10-271) contains supplementary material, which is available to authorized users.
There are no competing interests to declare.
VWD participated in study design, carried out sample processing and spectroscopy, completed statistical analysis and drafted the manuscript. DES was involved in study design and coordination, and helped to draft the manuscript. DE participated in study design and helped to draft the manuscript. MBS conceived the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Esophageal adenocarcinoma (EAC) often presents at a late, incurable stage, and mortality has increased substantially, due to an increase in incidence of EAC arising out of Barrett’s esophagus. When diagnosed early, however, the combination of surgery and adjuvant therapies is associated with high cure rates. Metabolomics provides a means for non- invasive screening of early tumor-associated perturbations in cellular metabolism.
Urine samples from patients with esophageal carcinoma (n = 44), Barrett’s esophagus (n = 31), and healthy controls (n = 75) were examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx software permitted quantification of 66 distinct metabolites. Unsupervised (principal component analysis) and supervised (orthogonal partial least-squares discriminant analysis OPLS-DA) multivariate pattern recognition techniques were applied to discriminate between samples using SIMCA-P+ software. Model specificity was also confirmed through comparison with a pancreatic cancer cohort (n = 32).
Clear distinctions between esophageal cancer, Barrett’s esophagus and healthy controls were noted when OPLS-DA was applied. Model validity was confirmed using two established methods of internal validation, cross-validation and response permutation. Sensitivity and specificity of the multivariate OPLS-DA models were summarized using a receiver operating characteristic curve analysis and revealed excellent predictive power (area under the curve = 0.9810 and 0.9627 for esophageal cancer and Barrett’s esophagus, respectively). The metabolite expression profiles of esophageal cancer and pancreatic cancer were also clearly distinguishable with an area under the receiver operating characteristics curve (AUROC) = 0.8954.
Urinary metabolomics identified discrete metabolic signatures that clearly distinguished both Barrett’s esophagus and esophageal cancer from controls. The metabolite expression profile of esophageal cancer was also discrete from its precursor lesion, Barrett’s esophagus. The cancer-specific nature of this profile was confirmed through comparison with pancreatic cancer. These preliminary results suggest that urinary metabolomics may have a future potential role in non-invasive screening in these conditions.
Additional file 1: 1 H-Chemical Shift (ppm relative to DSS-d6) and Corresponding Multiplicities For All Identified Metabolites. (PDF 48 KB)12957_2012_1230_MOESM1_ESM.pdf
Additional file 2: PCA Score Plot of Urinary Metabolite Profiles Derived from Esophageal Carcinoma and Healthy Controls. Esophageal cancer samples are represented by red triangles and blue circles depict controls. Two-component model based on 53 measured metabolites. (PDF 106 KB)12957_2012_1230_MOESM2_ESM.pdf
Additional file 3: OPLS-DA Loading Plot OPLS-DA of Metabolite Profiles Derived From BE and Healthy Controls. (PDF 115 KB)12957_2012_1230_MOESM3_ESM.pdf
Additional file 4: OPLS-DA Loading Plot of Metabolite Profiles Derived From EAC and BE. (PDF 116 KB)12957_2012_1230_MOESM4_ESM.pdf
Additional file 5: OPLS-DA Loading Plot of Metabolite Profiles Derived From Esophageal and Pancreatic Cancer. (PDF 128 KB)12957_2012_1230_MOESM5_ESM.pdf
Additional file 6: Exogenous Metabolites with Source Description. (PDF 58 KB)12957_2012_1230_MOESM6_ESM.pdf
Additional file 7: OPLS-DA Score Plot of Metabolite Profiles Derived from BE and Healthy Controls with Corresponding ROC Curve Analysis. A) Supervised OPLS-DA score plot. Two-component model based on 53 measured urinary metabolites. BE is represented by red triangles and controls are depicted by blue circles. B) Corresponding ROC curve generated using cross-validated predicted-Y values of OPLS-DA model. AUROC = 0.9627. (PDF 78 KB)12957_2012_1230_MOESM7_ESM.pdf
Additional file 8: OPLS-DA Score Plot of Metabolite Profiles Derived from EAC and BE with Corresponding ROC Curve Analysis. A) Supervised OPLS-DA score plot. Two-component model based on 53 measured urinary metabolites. BE is represented by blue circles triangles and EAC is depicted by red triangles. B) Corresponding ROC curve generated using cross-validated predicted-Y values of OPLS-DA model. AUROC = 0.9430. (PDF 74 KB)12957_2012_1230_MOESM8_ESM.pdf
Additional file 9: OPLS-DA Score Plot Depicting Cancer Specificity of Urinary Metabolomic Profiles and Corresponding ROC Curve Analysis. A) Urinary metabolomic profiles of patients with esophageal carcinoma represented by blue squares, and pancreatic ductal adenocarcinoma, depicted by red triangles. B) Corresponding ROC curve generated using cross-validated predicted-Y values of OPLS-DA model. AUROC = 0.8954. (PDF 85 KB)12957_2012_1230_MOESM9_ESM.pdf
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- Urinary metabolomic signature of esophageal cancer and Barrett’s esophagus
Vanessa W Davis
Daniel E Schiller
Michael B Sawyer
- BioMed Central
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