Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings
Bibliographic record
Abstract
BACKGROUND: African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa). OBJECTIVE: To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test. METHODS: Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects. RESULTS: Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set. CONCLUSIONS: Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".