Micro-RNA Profiling of Exosomes from Marrow-Derived Mesenchymal Stromal Cells in Patients with Acute Myeloid Leukemia: Implications in Leukemogenesis
Bibliographic record
Abstract
Gene regulatory networks in AML may be influenced by microRNAs (miRs) contained in exosomes derived from bone marrow mesenchymal stromal cells (MSCs). We sequenced miRs from exosomes isolated from marrow-derived MSCs from patients with AML (n = 3) and from healthy controls (n = 3; not age-matched). Known targets of mIRs that were significantly different in AML-derived MSC exosomes compared to controls were identified. Of the five candidate miRs identified by differential packaging in exosomes, only miR-26a-5p and miR-101-3p were significantly increased in AML-derived samples while miR-23b-5p, miR-339-3p and miR-425-5p were significantly decreased. Validation of the predicted change in gene expression of the potential targets was investigated by interrogating gene expression levels from public datasets of marrow-derived CD34-selected cells from patients with AML (n = 69) and healthy donors (n = 40). Two molecules with decreased gene expression in AML (EZH2 and GSK3β) were predicted by the miR profiling and have been previously implicated in AML while three molecules were increased in AML-derived cells and have not been previously associated with leukemogenesis (KRBA2, RRBP1 and HIST2H 2BE). In summary, profiling miRs in exosomes from AML-derived MSCs allowed us to identify candidate miRs with potential relevance in AML that could yield new insights regarding leukemogenesis or new treatment strategies.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".