Urinary metabolomics of gastric cancer.
Bibliographic record
Abstract
22 Background: Gastric cancer (GC) has 70-75% mortality, attributable to delayed diagnosis. There is no standard screening in North America. Metabolomics is a systems biology approach to measure low molecular weight chemicals (metabolites) in body fluids or tissues to provide a phenotypic “fingerprint” of disease etiology. In this preliminary study it was hypothesized that metabolic profiling of urine samples using 1H-NMR spectroscopy could discriminate between resectable gastric adenocarcinoma (GC), benign gastric disease (BN), and healthy (HE) patients (pts). Methods: Midstream urine samples were collected, processed, and biobanked at -80°C, from 30 BN, 30 HE and 16 of 29 GC pts visiting three Edmonton clinics from August 2013 – January 2014. Thirteen of 29 samples were retrieved from a 2009-13 GC biobank. Samples were matched on age, gender and BMI. Using a validated standard operating procedure each sample was analyzed using high resolution 1H-NMR spectroscopy. Resulting spectral traces were converted into annotated and quantified metabolite profiles of 58 metabolites. Univariate and multivariate statistical analysis uncovered a disease specific biomarker profile. Partial Least Squares Discriminant Analysis (PLS-DA) developed a GC vs. HE discriminative model. A Receiver Operator Characteristic (ROC) curve was constructed. Results: There was no significant difference in metabolite profiles between GC and BN pts. However, univariate analysis revealed 13 metabolites that differed significantly between GC and HE (p<0.05). Correlation analysis, followed by PLS-DA produced a discriminative model with an area under ROC curve of 0.996, such that for a specificity of 100% the corresponding sensitivity was 93%. Conclusions: GC pts have a distinct urinary metabolite profile compared to HE controls; however in this study metabolic profiling was unable to discriminate GC from BN pts. This was probably due to sample size and phenotypic heterogeneity of BN patients. This preliminary study shows clinical potential for metabolic profiling for early GC detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".