Genome-wide miRNA profiling of mantle cell lymphoma reveals a distinct subgroup with poor prognosis
Bibliographic record
Abstract
miRNA deregulation has been implicated in the pathogenesis of mantle cell lymphoma (MCL). Using a high-throughput quantitative real-time PCR platform, we performed miRNA profiling on cyclin D1-positive MCL (n = 30) and cyclin D1-negative MCL (n = 7) and compared them with small lymphocytic leukemia/lymphoma (n = 12), aggressive B-cell lymphomas (n = 138), normal B-cell subsets, and stromal cells. We identified a 19-miRNA classifier that included 6 up-regulated miRNAs and 13 down regulated miRNA that was able to distinguish MCL from other aggressive lymphomas. Some of the up-regulated miRNAs are highly expressed in naive B cells. This miRNA classifier showed consistent results in formalin-fixed paraffin-embedded tissues and was able to distinguish cyclin D1-negative MCL from other lymphomas. A 26-miRNA classifier could distinguish MCL from small lymphocytic leukemia/lymphoma, dominated by 23 up-regulated miRNAs in MCL. Unsupervised hierarchical clustering of MCL patients demonstrated a cluster characterized by high expression of miRNAs from the polycistronic miR17-92 cluster and its paralogs, miR-106a-363 and miR-106b-25, and associated with high proliferation gene signature. The other clusters showed enrichment of stroma-associated miRNAs, and also had higher expression of stroma-associated genes. Our clinical outcome analysis in the present study suggested that miRNAs can serve as prognosticators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".