Progression-Free Survival in Ovarian Cancer Is Reflected in Epigenetic DNA Methylation Profiles
Bibliographic record
Abstract
OBJECTIVE: Many patients with ovarian cancer disease relapse within 6 months after adjuvant chemotherapy, with a limited prognosis. Epigenetic modifications have been shown to play an important role in tumor development and formation. Therefore, global analysis of DNA methylation patterns might reveal specific CpG sites that correlate with progression-free interval (PFI) after therapy. METHODS: Twenty samples of advanced ovarian cancer with a predominantly serous papillary histological subtype were subjected to DNA methylation profiling. Illumina HumanMethylation27 BeadChip technology was used for simultaneous analysis of 27,578 CpG sites in >14,000 genes. RESULTS: Differential DNA methylation of various cytosines correlated with PFI. However, this becomes only significant by classification according to PFI with a cutoff of >28 months. Longer survival was associated with hypomethylation at specific CpG sites (e.g. GREB1, TGIF and TOB1) and hypermethylation in other genes (e.g. TMCO5, PTPRN and GUCY2C). Gene ontology analysis revealed that differentially methylated genes were significantly overrepresented in the categories telomere organization, mesoderm development and immune regulation. CONCLUSION: Epigenetic modifications at specific CpG sites correlate with PFI in ovarian cancer. Therefore, such analysis might be of prognostic value.
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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 | 0.000 | 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".