Exploring the potential of mucin 13 (MUC13) as a biomarker for carcinomas and other diseases
Bibliographic record
Abstract
BACKGROUND: Mucin 13 (MUC13) is a cell surface glycoprotein aberrantly expressed in a variety of epithelial carcinomas. Thus far, the role of MUC13 in various diseases remains elusive. To the best of our knowledge, this is the first study to examine the potential of MUC13 as a serum biomarker in a variety of carcinomas and other conditions. METHODS: We developed a recombinant MUC13 protein, mouse monoclonal antibodies and enzyme immunoassay (ELISA) for MUC13. We used this assay to measure MUC13 levels in the supernatants of cancer cell lines and a large cohort of serum samples from healthy and diseased individuals. RESULTS: MUC13 is secreted from cancer cell lines, with highest levels found in ovarian cancer cell lines. MUC13 levels in human sera were significantly increased in patients with renal failure and 20%-30% of patients with ovarian, liver, lung and other cancers. MUC13 was also elevated in 70% of patients with active cutaneous melanoma, but not uveal melanoma. Furthermore, we identified significant MUC13 elevations in the serum of patients with vasculitis (ANCA-positive) autoantibodies, but not in those with inflammatory bowel disease. CONCLUSIONS: Serum MUC13 is frequently elevated not only in a variety of malignant cases but also in some benign pathologies, thus appearing to be a non-specific disease biomarker. Nonetheless, serum MUC13 is clearly highly elevated in some carcinoma patients, and its relationship with tumor progression in this context warrant further research. Future studies that examine the correlation between serum MUC13 levels to stage of cancer could elucidate prognostic potential.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".