From gene profiling to diagnostic markers: IL‐18 and FGF‐2 complement CA125 as serum‐based markers in epithelial ovarian cancer
Bibliographic record
Abstract
We used an oligonucleotide-based DNA microarray to identify potential markers in 39 primary cultures of ovarian cancer specimens compared with 11 primary cultures of normal ovarian epithelia. Differential gene expression of IL-18 and FGF-2 was validated on a subset of samples by quantitative PCR and by IHC, using an independent tissue array of 90 cores of 20 normal ovarian surface epithelia and 70 EOCs representing different grades and pathologies of ovarian disease. We further compared, by ELISA, these two markers with CA125 in sera from 25 cancer-free and 47 ovarian cancer patients. IL-18 and FGF-2 proteins were significantly elevated in tumor tissues (p<0.04) and sera (p<0.05) from patients with ovarian cancer. In combination, the three markers (IL-18, FGF-2, and CA125) showed similar sensitivity in scoring for ovarian cancer (35/45 patients) compared to that of CA125 alone (37/45) and significantly improved the specificity of detection (20/25 patients) compared to each marker individually (15/25 for CA125; 18/25 FGF-2; 16/25 for IL-18). In conclusion we show that a combination of the three serum markers (IL-18, FGF-2 and CA125) is associated with EOC, with higher specificity than CA125 alone. Prospective studies with a large cohort of susceptible ovarian cancer patients will be required to expand these findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".