Exploring Targets of TET2-mediated Methylation Reprogramming as Potential Therapeutic Targets and Discriminators of Prostate Cancer Progression
Bibliographic record
Abstract
Aberrant cytosine methylation is one of the most common alterations in cancer, with global hypomethylation and regional hypermethylation characterizing many different malignancies, including prostate cancer. In normal cells, master methylation regulators, the ten-eleven translocase (TET) family of enzymes, demethylate genes by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine. Although lowered expression of all three TETs is common in prostate cancer, TET2 in particular plays a central role through its interaction with – and repression by – the androgen receptor. Loss of TET2 is associated with decreased cancer-specific survival, while missense alterations of TET2 are linked to metastatic disease. In this thesis, genome-wide targets of TET2 were examined via integrative (hydroxy)methylation and expression analysis to first identify how locus-specific epigenetic patterns affect prostate cancer cells and phenotype. Subsequently, genes regulated by TET2 were assessed to determine their individual and combinatorial utility as potential tumor suppressors or oncogenic factors in disease. In contrast to the paradigm of universal hydroxymethylation loss in cancer, locus-specific intronic retention in genes related to basic cellular function and intergenic gain of hydroxymethylcytosine marks proximal to androgen-related genes was observed in 22Rv1 prostate cancer cell lines. CRISPR-Cas9 based TET2 knockout in prostate cells identified high-confidence genes specifically mediated by TET2 methylation reprogramming. Expression loss of three such genes – ASB2, NUDT10, and SRPX – or increased promoter methylation of NRG1 was significantly (p
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".