Comprehensive Analysis of Conditioned Media from Ovarian Cancer Cell Lines Identifies Novel Candidate Markers of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer remains a deadly threat to women as the disease is often diagnosed in the late stages when the chance of survival is low. There are no good biomarkers available for early detection and only a few markers have shown clinical utility for prognosis, response to therapy and disease recurrence. We mined conditioned media of four ovarian cancer cell lines (HTB75, TOV-112D, TOV-21G and RMUG-S) by two-dimensional liquid chromatography-mass spectrometry. Each cell line represented one of the major histological types of epithelial ovarian cancer. We identified 2039 proteins from which 228 were extracellular and 192 were plasma membrane proteins. Within the latter list, we identified several known markers of ovarian cancer including three that are well established, namely, CA-125, HE4, and KLK6. The list of 420 extracellular and membrane proteins was cross-referenced with the proteome of ascites fluid to generate a shorter list of 51 potential biomarker candidates. According to Ingenuity Pathway Analysis, two of the top 10 diseases associated with the list of 51 proteins were cancer and reproductive diseases. We selected nine proteins for preliminary validation using 20 serum samples from healthy women and 10 from women with ovarian cancer. Of the nine proteins, clusterin (increase) and IGFBP6 (decrease) showed significant differences between women with or without ovarian cancer. We conclude that in-depth proteomic analysis of cell culture supernatants of ovarian cancer cell lines can identify potential ovarian cancer biomarkers that are worth further clinical validation.
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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 | 0.001 |
| 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) | 0.000 | 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 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".