Impact of hemochromatosis gene (<i>HFE</i>) mutations on epithelial ovarian cancer risk and prognosis
Bibliographic record
Abstract
Cancer cells require large amounts of micronutrients, particularly iron, for their rapid growth and frequent divisions. Cellular iron uptake is regulated by the transferrin receptor and the hemochromatosis protein (HFE) system. Two frequent mutations in the HFE gene, H63D and C282Y, are associated with hemochromatosis type I, an inherited iron overload disease and, possibly, with cancer. In this study, we evaluated the frequency of the H63D and C282Y mutations in a cohort of 677 consecutive cases of woman with gynecological pathologies. Cases included 80 women with tumor-free pathologies normal ovary (NOV), 124 with benign ovarian tumors (BOV), 96 with epithelial ovarian cancer (EOC) tumors of low malignant potential (LPM), 264 with invasive tumors of the ovary (TOV) and 113 with endometrial cancer. We found that the C282Y allele frequency in EOC patients was higher than that in the control NOV group (5.8% vs. 1.3%, p < 0.001) and was associated with an increased risk of ovarian cancer (OR = 4.88; 95% CI 1.15-20.61; p = 0.018). The effect of the two HFE mutations on patient survival was also analyzed. Kaplan-Meier analyses did not find any significant association between the H63D allele and patient survival. However, EOC patients with at least one C282Y allele had a decreased overall survival compared to those with no C282Y allele (p = 0.001). These results indicate that the C282Y mutation may increase the risk of developing ovarian cancer and may be further associated with poor outcomes.
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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 | 0.002 |
| 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".