Metabolomic signature of esophageal cancer.
Bibliographic record
Abstract
21 Background: Esophageal cancer is a pervasive malignancy, and early detection combined with newer therapeutic targets could alter the landscape of this condition. Metabolomic profiling offers one such innovative opportunity. We applied metabolomic techniques to identify urinary metabolites uniquely associated with this condition. Methods: Urine samples from patients with histologically confirmed esophageal cancer (n=66) and healthy volunteers (n=25) were collected and examined using 1H-NMR spectroscopy. Targeted profiling of spectra using Chenomx NMR Suite 7.0 software permitted detection and quantification of 66 distinct metabolites. Unsupervised (principal component analysis, PCA) and supervised (partial least-squares discriminant analysis, PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of esophageal cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 59 metabolites in urines of healthy volunteers and esophageal cancer patients. Those metabolites contributing most class discriminating information included choline, urea, 2-aminobutyrate, and 3-hydoxybutyrate. Clear distinctions between patients with esophageal cancer and healthy controls were noted when PLS-DA was applied to the data set. Model parameters for both goodness of fit R2, and predictive capability Q2, were high (R2 = 0.867; Q2 = 0.732). Model validity was tested using response permutation and results were suggestive of excellent predictive power (see Figure). Conclusions: Urinary metabolomics identified a discrete signature associated with esophageal cancer compared to healthy controls. This profile has the potential to aid in diagnosis and development of new therapeutic targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".