Aberrant DNA methylation potentiates oncogenes’ expression and disease progression in ovarian cancer
Bibliographic record
Abstract
Aberrant DNA methylation potentiates oncogenes expression and disease progression in ovarian cancerEpithelial ovarian cancer (EOC) accounts for 4% of all cancers in women and is the leading cause of death from gynecologic malignancies.The molecular basis of EOC initiation and progression is still poorly understood.Previously, we have applied an epigenomics approach to investigate the possible implication of aberrant DNA methylation in EOC etiology.We used methylated DNA immunoprecipitation in combination with CpG island tiling arrays to characterize at high resolution the DNA methylation changes that occur in the genome of serous EOC tumors during disease progression.We found widespread DNA hypermethylation that occurs even in less invasive/early stages of ovarian tumorigenesis.In contrast, significant DNA hypomethylation was observed only in high-grade (G3) serous tumors.This approach led to the identification of novel EOC oncogenes, potentially modulated by epigenetic mechanisms (hypomethylation) in advanced EOC, and displaying implication in different mechanisms of EOC dissemination, including alterations in gene expression control (RUNX1, RUNX2), abnormal metabolism (BCAT1), aberrant Oglycosylation (GALNT3) and importantly, epithelial to mesenchymal transition (EMT) regulation (Ly75, GRHL2, HIC-5).These genes could represent new therapeutic targets and/or novel biomarkers indicative for EOC progression.Moreover, our data are indicative for the implication of aberrant DNA methylation in EMT-mediated EOC progression.
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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".