Mucin 13 (MUC13) as a candidate biomarker for ovarian cancer detection: potential to complement CA125 in detecting non-serous subtypes
Bibliographic record
Abstract
OBJECTIVES: Ovarian cancer is the most lethal gynecological malignancy in developed countries. One of the key associations with the high mortality rate is diagnosis at late stages. This clinical limitation is primarily due to a lack of distinct symptoms and detection at the early stages. The ovarian cancer biomarker, CA125, is mainly effective for identifying serous ovarian carcinomas, leaving a gap in non-serous ovarian cancer detection. Mucin 13 (MUC13) is a transmembrane, glycosylated protein with aberrant expression in malignancies, including ovarian cancer. We explored the potential of MUC13 to complement CA125 as an ovarian cancer biomarker, by evaluating its ability to discriminate serous and non-serous subtypes of ovarian cancer at FIGO stages I-IV from benign conditions. METHODS: We used our newly developed, high sensitivity ELISA to measure MUC13 protein in a large, well-defined cohort of 389 serum samples from patients with ovarian cancer and benign conditions. RESULTS: MUC13 and CA125 serum levels were elevated in malignant compared to benign cases (p<0.0001). Receiver-operating characteristic (ROC) curve analysis showed similar area under the curve (AUC) of 0.74 (MUC13) and 0.76 (CA125). MUC13 concentrations were significantly higher in mucinous adenocarcinomas compared to benign controls (p=0.0005), with AUC of 0.80. MUC13 and CA125 showed significant elevation in early-stage cases (stage I-II) in relation to benign controls (p=0.0012 and p=0.014, respectively). CONCLUSIONS: We report the novel role of MUC13 as a serum ovarian cancer biomarker, where it could complement CA125 for detecting some subtypes of non-serous ovarian carcinoma and early-stage disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".