Genome-wide identification of human microRNAs located in leukemia-associated genomic alterations
Bibliographic record
Abstract
Cytogenetic alterations, such as amplifications, deletions, or translocations, contribute to myeloid malignancies. MicroRNAs (miRNAs) have emerged as critical regulators of hematopoiesis, and their aberrant expression has been associated with leukemia. Genomic regions containing sequence alterations and fragile sites in cancers are enriched with miRNAs; however, the relevant miRNAs within these regions have not been evaluated on a global basis. Here, we investigated miRNAs relevant to acute myeloid leukemia (AML) by (1) mapping miRNAs within leukemia-associated genomic alterations in human AML cell lines by high-resolution genome arrays and (2) evaluating absolute expression of these miRNAs by massively parallel small RNA sequencing. Seventy-seven percent (542 of 706) of miRNAs mapped to leukemia-associated copy-number alterations in the cell lines; however, only 18% (99 of 542) of these miRNAs are expressed above background levels. As evidence that this subset of miRNAs is relevant to leukemia, we show that loss of 2 miRNAs identified in our analysis, miR-145 and miR-146a, results in leukemia in a mouse model. Small RNA sequencing identified 28 putative novel miRNAs, 18 of which map to leukemia-associated copy-number alterations. This detailed genomic and small RNA analysis points to a subset of miRNAs that may play a role in myeloid malignancies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".