Epigenetic silencing of tumor suppressor genes: Paradigms, puzzles, and potential
Bibliographic record
Abstract
Cancer constitutes a set of diseases with heterogeneous molecular pathologies. However, there are a number of universal aberrations common to all cancers, one of these being the epigenetic silencing of tumor suppressor genes (TSGs). The silencing of TSGs is thought to be an early, driving event in the oncogenic process. With this in consideration, great efforts have been made to develop small molecules aimed at the restoration of TSGs in order to limit tumor cell proliferation and survival. However, the molecular forces that drive the broad epigenetic reprogramming and transcriptional repression of these genes remain ill-defined. Undoubtedly, understanding the molecular underpinnings of transcriptionally silenced TSGs will aid us in our ability to reactivate these key anti-cancer targets. Here, we describe what we consider to be the five most logical molecular mechanisms that may account for this widely observed phenomenon: 1) ablation of transcription factor binding, 2) overexpression of DNA methyltransferases, 3) disruption of CTCF binding, 4) elevation of EZH2 activity, 5) aberrant expression of long non-coding RNAs. The strengths and weaknesses of each proposed mechanism is highlighted, followed by an overview of clinical efforts to target these processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".