Regulation and Function of the <i> <scp>MYC</scp> </i> Oncogene
Bibliographic record
Abstract
Abstract One of the most acclaimed features of the MYC oncogene family ( MYC, MYCL1 and MYCN ) is their prolific deregulation in cancer, which is often associated with poor prognosis and refractory disease. Multiple mechanisms can deregulate their expression in cancer, including chromosomal translocation, enhanced messenger ribonucleic acid (mRNA) and protein stability, gene amplification or enhancer hijacking. This MYC family of nuclear transcription factors regulates the expression of a multitude of target genes to control many critically important fundamental biological processes, including cellular proliferation, metabolism, apoptosis and embryonic development. MYC proteins are highly regulated, and many factors have been reported to control stability and activity via post‐translational modifications (PTMs). Decades of research into this potent oncogene family have revealed that while directly inhibiting MYC proteins in cancer remains challenging, there are multiple strategies to indirectly inhibit MYC in cancer. Developing such inhibitors to target MYC would have profound impact on patient care and outcome. Key Concepts The MYC family of oncogenes, composed of MYC, MYCN and MYCL1 , encode nuclear basic helix‐loop‐helix transcription factors. MYC family proteins contain highly conserved regions termed MYC boxes. MYC regulates many transcriptional targets and can have wide‐reaching effects on the epigenome and total cellular RNA content. MYC is essential for cellular proliferation and is a potent oncogene when it is deregulated in cancer. Deregulated, often elevated, MYC levels have been shown to drive tumourigenesis in many in vivo models. MYC activity and stability is regulated by post‐translational modifications, including phosphorylation, ubiquitylation, SUMOylation and acetylation.
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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) | 0.003 | 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".