Reflecting on 30 years of miRNA biology in malignant hematology: current challenges and future directions
Bibliographic record
Abstract
ABSTRACT: MicroRNAs (miRNAs) are essential regulators of hematopoiesis, influencing stem cell maintenance, lineage specification, and differentiation. While their dysregulation has been widely implicated in hematological malignancies such as acute myeloid leukemia, progress toward clinical translation has been hindered by methodological inconsistencies, oversimplified interpretations, and model limitations. This viewpoint discusses the context-dependent nature of miRNA-mRNA interactions, the influence of isomiRs, and the impact of RNA-binding proteins and epitranscriptomic modifications on miRNA activity. We highlight the limitations of commonly used bulk sequencing and reductionist models, and advocate for more physiologically relevant systems, including hematopoietic organoids, single-cell and spatial transcriptomics, and CRISPR-based functional assays. Furthermore, we discuss advances in miRNA-targeted therapeutics, such as lipid nanoparticle delivery and anti-miRs. By integrating emerging technologies with standardized methodologies and biological complexity, miRNA research in hematology will uncover new regulatory mechanisms and therapeutic vulnerabilities, offering a robust path toward diagnostic, prognostic, and treatment applications.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".