Analysis of Circulating Tumor Cells in Ovarian Cancer and Their Clinical Value as a Biomarker
Bibliographic record
Abstract
BACKGROUND/AIMS: Monitoring the appearance and progression of tumors are important for improving the survival rate of patients with ovarian cancer. This study aims to examine circulating tumor cells (CTCs) in epithelial ovarian cancer (EOC) patients to evaluate their clinical significance in comparison to the existing biomarker CA125. METHODS: Immuomagnetic bead screening, targeting epithelial antigens on ovarian cancer cells, combined with multiplex reverse transcriptase-polymerase chain reaction (Multiplex RT-PCR) was used to detect CTCs in 211 samples of peripheral blood (5 ml) from 109 EOC patients. CTCs and CA125 were measured in serial from 153 blood and 153 serum samples from 51 patients and correlations with treatment were analyzed. Immunohistochemistry was used to detect the expression of tumor-associated proteins in tumor tissues and compared with gene expression in CTCs from patients. RESULTS: CTCs were detected in 90% (98/109) of newly diagnosed patients. In newly diagnosed patients, the number of CTCs was correlated with stage (p=0.034). Patients with stage IA-IB disease had a CTC positive rate of 93% (13/14), much higher than the CA125 positive rate of only 64% (9/14) for the same patients. The numbers of CTCs changed with treatment, and the expression of EpCAM (p=0.003) and HER2 (p=0.035) in CTCs was correlated with resistance to chemotherapy. Expression of EpCAM in CTCs before treatment was also correlated with overall survival (OS) (p=0.041). CONCLUSION: Detection of CTCs allows early diagnose and expression of EpCAM in CTC positive patients predicts prognosis and should be helpful for monitoring treatment.
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 | 0.000 | 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 | 0.000 | 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".