Ascites and Serum Interleukin-10 Levels as a Prognostic Tool for Ovarian Cancer Outcomes
Bibliographic record
Abstract
Interleukin-10 (IL-10) has been shown to be present at high levels in the ascites of ovarian cancer (OC) patients; however, little is known about its prognostic value. We sought to correlate IL-10 levels in ascites and sera of OC patients with clinicopathologic characteristics and oncologic outcomes. IL-10 levels and clinical data from biobanked ascites and serum samples of OC patients were evaluated. Receiver operating characteristic curves were used to quantify marker performance and identify IL-10-high and IL-10-low groups. Correlations between IL-10 levels and clinicopathologic data were performed. Survival outcomes were calculated, while the factors affecting them were also investigated. A total of 106 patients had ascites samples, of which 44 serum samples were also available. Mean ascites IL-10 levels were significantly higher in patients with serous histology compared to endometrioid histology (p = 0.024). Fold-change in ascites IL-10 during treatment positively correlated with clinical response, as determined by a change in serum cancer antigen (CA)-125 levels (p = 0.0126). Median progression-free survival (PFS) and overall survival (OS) were shorter in patients with high compared with low ascites IL-10 levels (PFS: 18 versus 60 months; p = 0.007, OS: 42 versus 85 months; p = 0.029). A significant positive correlation was seen between ascites and sera IL-10 levels (p = 0.019). In multivariable analyses, a high ascites IL-10 level was associated with a significantly worse prognosis (PFS hazard ratio (HR) = 1.93; p = 0.02). Patients with high ascites levels of IL-10 have worse outcomes, which are likely reflective of the immunosuppressive effect of IL-10. This highlights its potential role as an immunomodulator in the tumor microenvironment, leading to OC immune evasion.
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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 | 0.001 | 0.003 |
| 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".