Bibliographic record
Abstract
Emilia L Lim,1 Marco A Marra1,21Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada; 2Department of Medical Genetics, University of British Columbia, Vancouver, BC, CanadaAbstract: B-cell non-Hodgkin lymphomas (NHLs) are lymphoproliferative disorders that can arise at different stages of B-cell development. Even though molecular classification of NHL has allowed for more accurate recognition of distinct aggressive lymphoma subtypes, many patients still fail to respond to standard therapy. As such, there is a need to identify biomarkers and therapeutic targets that can lead to more specific treatments for each NHL patient's disease. MicroRNAs (miRNAs) are small, 17–25 nt RNA molecules that regulate gene expression at the posttranscriptional level. miRNA expression and function is often coordinately dysregulated in NHL, and consequently results in each NHL disease type harboring a distinct miRNA expression signature. miRNA dysregulation may be a consequence of several mechanisms, ranging from dysregulation of the DNA sequences encoding the miRNA to transcriptional regulation of miRNA loci, to dysregulation of the miRNA biogenesis pathway or dysregulation of messenger RNA (mRNA) targets. This coordinated dysregulation of miRNA expression systematically results in the activation of several oncogenic pathways, and consequently the reprogramming of B-cell NHL transcriptomes. The widespread dysregulation of miRNAs suggests that miRNAs may be used as a diagnostic and prognostic tool, and also as actionable drug targets. In this review, we summarize the miRNA profiles of the most common B-cell NHLs, discuss the causes and consequences of miRNA dysregulation, and consider the prospects of miRNA-based biomarkers and therapeutic targets in NHL.Keywords: miRNA, non-Hodgkin lymphoma, dysregulation, therapy
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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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".