MicroRNA Signatures: Illuminating Minimal Residual Disease Monitoring in Juvenile Myelomonocytic Leukemia - A Review
Bibliographic record
Abstract
Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric myelodysplastic/myeloproliferative neoplasm characterized by RAS pathway mutations and significant heterogeneity. Minimal residual disease (MRD) monitoring is crucial for evaluating treatment response and predicting relapse risk. MicroRNA (miRNAs), small non-coding RNAs with pivotal roles in gene regulation, have emerged as promising biomarkers for JMML MRD detection. This review explores the mechanistic role of miRNAs in JMML pathogenesis, emphasizing their diagnostic, prognostic, and therapeutic potential. Dysregulated miRNA profiles correlate with distinct JMML subgroups and disease progression, suggesting utility in non-invasive MRD monitoring. Emerging evidence highlights miR-150-5p as a tumor suppressor targeting STAT5b and its therapeutic potential in ameliorating JMML's aberrant signaling pathways. We compare traditional MRD methods, such as flow cytometry and polymerase chain reaction (PCR), with miRNA-based techniques, underscoring the latter's superior sensitivity, specificity, and non-invasiveness. Recent advances in miRNA profiling technologies, including next-generation sequencing and digital PCR, enable precise detection of residual leukemic cells and support personalized treatment approaches. Despite significant progress, challenges persist in standardizing miRNA-based assays and validating their clinical utility. Ethical considerations, including patient privacy and informed consent, remain critical for integrating miRNA diagnostics into routine care. This review provides a comprehensive synthesis of current knowledge on miRNA signatures in JMML, illuminating their transformative potential in MRD monitoring and paving the way for innovative therapeutic strategies.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".