The metabolomic signature of pancreatic cancer in urine.
Bibliographic record
Abstract
180 Background: Pancreatic cancer is one of the leading causes of cancer-related death, due partly to the lack of early detection and screening methods. Metabolomics, the newest of the “omics” sciences, provides a means for non-invasive screening of early tumor associated perturbations in cellular metabolism. We applied metabolomic techniques to identify urinary metabolites capable of facilitating diagnosis of pancreatic cancer. Methods: Urine samples from pancreatic cancer patients (n=55) 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 quantification of 66 metabolites. Unsupervised (PCA) and supervised (PLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra of pancreatic cancer patients and healthy volunteers using SIMCA-P (version 11, Umetrics, Umeå, Sweden). Results: Significant differences were found when comparing concentrations of 66 metabolites in urines of healthy volunteers and pancreatic cancer patients. Those metabolites contributing the most class discriminating information included choline, 2-aminobutyrate, urea and 2-oxoglutarate. Clear distinctions between pancreatic cancer patients 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 = 0.829; Q2 = 0.76). Model validity was tested using response permutation and results were suggestive of excellent predictive power. Application of PLS-DA to the data set also revealed clear discrimination of Stage I-III and Stage IV disease states, with the following model parameters, R2 = 0.62; Q2 =0.45. Conclusions: Urinary metabolomics detected clear differences in metabolic profiles of pancreatic cancer patients and healthy volunteers. Early results presented here suggest that metabolomic approaches may facilitate discovery of novel biomarkers capable of early disease detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".