Role of E2F transcription factor in oral cancer: Recent insight and advancements
Bibliographic record
Abstract
The family of mammalian E2F transcription factors (E2Fs) comprise of 8 members (E2F1-E2F8) classified as activators (E2F1-E2F3) and repressors (E2F4-E2F8) primarily regulating the expression of several genes related to cell proliferation, apoptosis and differentiation, mainly in a cell cycle-dependent manner. E2F activity is frequently controlled via the retinoblastoma protein (pRb), cyclins, p53 and the ubiquitin-proteasome pathway. Additionally, genetic or epigenetic changes result in the deregulation of E2F family genes expression altering S phase entry and apoptosis, an important hallmark for the onset and development of cancer. Although studies reveal E2Fs to be involved in several human malignancies, the mechanisms underlying the role of E2Fs in oral cancer lies nascent and needs further investigations. This review focuses on the role of E2Fs in oral cancer and the etiological factors regulating E2Fs activity, which in turn transcriptionally control the expression of their target genes, thus contributing to cell proliferation, metastasis, and drug/therapy resistance. Further, we will discuss therapeutic strategies for E2Fs, which may prevent oral tumor growth, metastasis, and drug resistance.
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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 | 0.002 | 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 | 0.002 |
| 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".