The Epigenetic Reader Methyl-CpG-Binding Protein 2 (MeCP2) Is an Emerging Oncogene in Cancer Biology
Bibliographic record
Abstract
Epigenetic mechanisms are gene regulatory processes that control gene expression and cellular identity. Epigenetic factors include the “writers”, “readers”, and “erasers” of epigenetic modifications such as DNA methylation. Accordingly, the nuclear protein Methyl-CpG-Binding Protein 2 (MeCP2) is a reader of DNA methylation with key roles in cellular identity and function. Research studies have linked altered DNA methylation, deregulation of MeCP2 levels, or MECP2 gene mutations to different types of human disease. Due to the high expression level of MeCP2 in the brain, many studies have focused on its role in neurological and neurodevelopmental disorders. However, it is becoming increasingly apparent that MeCP2 also participates in the tumorigenesis of different types of human cancer, with potential oncogenic properties. It is well documented that aberrant epigenetic regulation such as altered DNA methylation may lead to cancer and the process of tumorigenesis. However, direct involvement of MeCP2 with that of human cancer was not fully investigated until lately. In recent years, a multitude of research studies from independent groups have explored the molecular mechanisms involving MeCP2 in a vast array of human cancers that focus on the oncogenic characteristics of MeCP2. Here, we provide an overview of the proposed role of MeCP2 as an emerging oncogene in different types of human cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".