Establishment of an optimized CTC detection model consisting of EpCAM, MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics
Bibliographic record
Abstract
<sec><b>Objective</b>Emerging studies have demonstrated the promising clinical value of circulating tumor cells (CTCs) for diagnosis, disease assessment, treatment monitoring and prognosis in epithelial ovarian cancer. However, the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.</sec><sec><b>Methods</b>We enrolled 160 patients with epithelial ovarian cancer as the experimental group, and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group. We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens (EpCAM and MUC1), and used multiple reverse transcription-polymerase chain reaction (RT-PCR) detecting three markers (EpCAM, MUC1 and WT1) for quantification. And then we used a binary logistic regression analysis and focused on EpCAM, MUC1 and WT1 to establish an optimized CTC detection model.</sec><sec><b>Results</b>The sensitivity and specificity of the optimized model is 79.4% and 92.2%, respectively. The specificity of the CTC detection model is significantly higher than CA125 (92.2% <i>vs</i>. 82.2%, P=0.044), and the detection rate of CTCs was higher than the positive rate of CA125 (74.5% <i>vs.</i> 58.2%, P=0.069) in early-stage patients (stage I and II). The detection rate of CTCs was significantly higher in patients with ascitic volume ≥500 mL, suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy (P<0.05). The detection rate of CTC<sup>EpCAM</sup><sup>+</sup> and CTC<sup>MUC1+</sup> was significantly higher in chemo-resistant patients (26.3% <i>vs</i>. 11.9%; 26.4% <i>vs</i>. 13.4%, P<0.05). The median progression-free survival time for CTC<sup>MUC1+</sup> patients trended to be longer than CTC<sup>MUC1−</sup> patients, and overall survival was shorter in CTC<sup>MUC1+</sup> patients (P=0.043).</sec><sec><b>Conclusions</b>Our study presents an optimized detection model for CTCs, which consists of the expression levels of three markers (EpCAM, MUC1 and WT1). In comparison with CA125, our model has high specificity and demonstrates better diagnostic values, especially for early-stage ovarian cancer. Detection of CTC<sup>EpCAM+</sup> and CTC<sup>MUC1+</sup> had predictive value for chemotherapy resistance, and the detection of CTC<sup>MUC1+</sup> suggested poor prognosis.</sec>
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".