Combined loss of function of two different loci of miR-15/16 drives the pathogenesis of acute myeloid leukemia
Bibliographic record
Abstract
Double knockout of the two miR-15/16 loci in mouse resulted in the development of acute myeloid leukemia (AML). This result suggested that, at least, a fraction of human AMLs could be due to a similar mechanism. We analyzed the role of the two miR-15/16 clusters in 93 myelodysplastic syndrome (MDS) patients divided in three subgroups: patients with MDS, patients with MDS before transforming into AML (MDS-T), and patients with AML evolving from MDS (MDS-AML). Then, we tested 139 AML cases and 14 different AML cell lines by assessing microRNA (miRNA) expression, target protein expression, genetic loss, and silencing. MDS-T and MDS-AML patients show a reduction of the expression of miR-15a/-15b/-16 compared to MDS patients. Each miRNA can significantly predict MDS and MDS-T groups. Then, 79% of primary AMLs show a reduced expression of miR-15a and/or miR-15b. The expression of miR-15a/-15b/-16 significantly stratified AML patients in two prognostic classes. Furthermore, 40% of AML cell lines showed a combined loss of the expression of miR-15a/-15b and overexpression of their direct/indirect targets. As potential mechanisms involved in the silencing of the two miR-15/16 loci, we identified a genetic loss of miR-15a and miR-15b and silencing of these two loci by methylation. We identified a potential driver oncogenic role in the loss of expression of both miR-15/16 clusters in the progression of MDS into AML and in AML pathogenesis. The stratification of AML patients, based on miR-15/16 expression, can lead to targeted and combination therapies for the treatment of this incurable disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".