Altered DNA Methylation Landscapes of Polycomb-Repressed Loci Are Associated with Prostate Cancer Progression and ERG Oncogene Expression in Prostate Cancer
Bibliographic record
Abstract
PURPOSE: To assess differentially methylated "landscapes" according to prostate cancer Gleason score (GS) and ERG oncogene expression status, and to determine the extent of polycomb group (PcG) target gene involvement, we sought to assess the genome-wide DNA methylation profile of prostate cancer according to Gleason score and ERG expression. EXPERIMENTAL DESIGN: Genomic DNA from 39 prostate cancer specimens was hybridized to CpG island microarrays through differential methylation hybridization. We compared methylation profiles between Gleason score and ERG expression status as well as Gleason score stratified by ERG expression status. In addition, we compared results from our dataset to publicly available datasets of histone modifications in benign prostate cells. RESULTS: We discovered hundreds of distinct differentially methylated regions (DMR) associated with increasing Gleason score and ERG. Furthermore, the number of DMRs associated with Gleason score was greatly expanded by stratifying samples into ERG-positive versus ERG-negative, with ERG-positive/GS-associated DMRs being primarily hypermethylated as opposed to hypomethylated. Finally, we found that there was a significant overlap between either Gleason score-related or ERG-hypermethylated DMRs and distinct regions in benign epithelial cells that have PcG signatures (H3K27me3, SUZ12) and lack active gene expression signatures (H3K4me3, RNA pol II). CONCLUSIONS: This work defines methylation landscapes of prostate cancer according to Gleason score, and suggests that initiating genetic events may influence the prostate cancer epigenome, which is further perturbed as prostate cancer progresses. Moreover, CpG islands with silent chromatin signatures in benign cells are particularly susceptible to prostate cancer-related hypermethylation.
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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 | 0.001 |
| 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".