Endometrioid ovarian cancer and endometriotic cells exhibit the same alteration in the expression of interleukin-1 receptor II: To a link between endometriosis and endometrioid ovarian cancer
Bibliographic record
Abstract
AIM: Endometrioid carcinoma of the ovary is the third most common type of epithelial ovarian cancer. Endometrioid tumors as well as endometriotic implants are characterized by the presence of epithelial cells, stromal cells, or a combination of booth, that resemble the endometrial cells, suggesting a possible endometrial origin of these tumors. Th1 cytokines including interleukin (IL)-1 have been reported to be involved in both endometriosis and ovarian carcinogenesis. We assessed the expression of receptors of IL-1 (IL-1RI and IL-1RII, the signal transducer and the specific inhibitor of IL-1, respectively) in cells of the most common subtypes of ovarian cancer compared to endometrial cells. MATERIAL & METHODS: IL1-Rs expression was analyzed at the levels of the protein and mRNA using immunofluorescent and real-time polymerase chain reaction methods, respectively. RESULTS: We showed that endometrioid cells exhibit a specific decrease of IL-1RII expression, whereas IL-1RI was constantly expressed in all studied cell subtypes. CONCLUSION: As already reported in endometriotic cells, endometrioid ovarian cancer cells exhibit the same alteration in the expression of IL-1RII, a key protector against tumorigenic effects of IL-1. Our findings highlight a common signature between endometrioid ovarian cancer and implants of endometriosis, which needs to be fully explored.
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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 | 0.000 | 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 | 0.000 |
| 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".