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.
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 | 0.000 | 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) | 0.000 | 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".