Gene methylation profiles as prognostic markers in ovarian clear cell and endometrioid adenocarcinomas
Bibliographic record
Abstract
Ovarian cancer is a cancer of high mortality. Aberrant gene methylation of tumor suppressor genes has been shown to be related to the development of malignancy. This study aimed to investigate the methylation of various genes in ovarian clear cell adenocarcinoma (OCCA) and ovarian endometrioid adenocarcinoma (OEA) and evaluate methylation biomarkers in terms of patient chemo-response and outcome. Eight candidate genes from 66 OCCA and 51 OEA patients were evaluated by methylation-specific polymerase chain reaction and capillary electrophoresis. Clinico-pathological parameters and patient outcomes were analyzed. The frequencies of gene methylation in RASSF1A (79% vs. 59%, p=0.025), E-cadherin (30% vs. 10%, p=0.011), and DLEC1 (71% vs. 43%, p=0.003) were higher in the patients with OCCA than in those with OEA. The chemo-resistant group had a significantly higher percentage of E-cadherin methylation (36.7% vs. 16.1%, p=0.036) than the chemo-sensitive group. In multivariate analysis (log-rank test), advanced stage (4.79 [2.10-10.94], p<0.001) was the only risk factor for mortality. Those with methylation of more than two out of three genes (E-cadherin, DLEC1, and SFRP5) had a shorter disease-free survival (1.89 [1.07-3.32], p=0.028) and overall survival (3.29 [1.57-6.87], p=0.002) than those with methylation of one or no gene. In advanced-stage malignancies, those with more than two out of the three gene methylations also had a shorter overall survival (3.86 [1.63-9.09], p=0.002) than those with methylation of only one or no gene. Patients with OCCA have different patterns of gene methylation than those with OEA. Methylation of the E-cadherin, DLEC1 and SFRP5 genes can be a prognostic biomarker for OCCA and OEA.
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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 | 0.001 |
| 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".