Mechanisms of AML1-ETO Induced Transcription Factor Dysregulation, Epigenetic Modification, and Immune System Evasion in Pre-Leukemic Stem Cells
Bibliographic record
Abstract
Introduction: Acute myeloid leukemia is a cancer of the bone marrow with a low survival rate of 15% among elderly individuals due to its rapid progression and lack of available treatment. Understanding different transcription factors that may contribute to the progression of leukemia and leukemogenesis is crucial in developing effective treatment plans to control the advancement of AML within patients. This paper aims to conduct a literature review where primary articles contain evidence exhibiting a link between transcriptional dysregulation in genes that code for regulatory proteins involved in cell cycle progression and irregularity present in pre-leukemic stem cells. Methods: Databases Medline and Embase were searched using keywords “leukemogenesis,” “pre-leukemic,” “AML1”, “Acute myeloid leukemia”, and “AML-ETO”. Only studies written in the English language that were published in peer-reviewed journals and human post-mortem/pathophysiological studies were considered in the review. The search emphasized studies published between 1998-2023 to include recent literature reported in the respective field. Results: AML1 regulates the expression of cell cycle checkpoint regulating proteins and proteins involved in hematopoietic differentiation in order to prevent asymmetric production of mutated hematopoietic stem cells (HSCs) as well as defective cells from progressing through the cycle via apoptosis. Chromosomal translocations allow for the AML1 gene to be fused with ETO, causing issues with transcriptional regulation, which work to continuously repress the function of this complex, preventing the transcription of necessary proteins needed to regulate the cell cycle. Implications: The fusion of AML1 and ETO causes the deregulation of the transcription complex, repressing the binding of transcription factors. This means that defective cells within hematopoiesis are not subject to apoptosis, leading to the production of pre-leukemic stem cells, which contributes to leukemogenesis. Understanding the mechanisms leading to the production of pre-leukemic stem cells opens up possible treatment channels from pharmacological and physiological approaches. In addition, leukemia can be identified and diagnosed at an earlier stage by identifying AML1 in relation to ETO as a precursor for AML.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".