Global microRNA expression profiling uncovers molecular markers for classification and prognosis in aggressive B-cell lymphoma
Bibliographic record
Abstract
We studied the global microRNA (miRNA) expression in diffuse large B-cell lymphoma (DLBCL; n = 79), Burkitt lymphoma (BL; n = 36), primary mediastinal B-cell lymphoma (PMBL; n = 12), B-cell lines (n = 11), and normal subsets of naïve B cells, centroblasts (CBs), and peripheral blood B cells along with their corresponding gene expression profiles (GEPs). The normal B-cell subsets have well-defined miRNA signatures. The CB miRNA signature was significantly associated with germinal center B-cell (GCB)-DLBCL compared with activated B-cell (ABC)-DLBCL (P = .002). We identified a 27-miRNA signature that included v-myc avian myelomatosis viral oncogene homolog (MYC) targets and enabled the differentiation of BL from DLBCL, a distinction comparable with the "gold standard" GEP-defined diagnosis. Distinct miRNA signatures were identified for DLBCL subgroups, including GCB-DLBCL, activated B-cell (ABC)-DLBCL, and PMBL. Interestingly, most of the unclassifiable-DLBCL by GEP showed a strong similarity to the ABC-DLBCL by miRNA expression profiling. Consistent results for BL and DLBCL subgroup classification were observed in formalin-fixed, paraffin-embedded tissue, making such tests practical for clinical use. We also identified predictive miRNA biomarker signatures in DLBCL, including high expression of miR-155, which is significantly associated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) treatment failure. This finding was further supported by the observation that high expression of miR-155 sensitizes cells to v-akt murine thymoma viral oncogene homolog-1 inhibitors in vitro, suggesting a novel treatment option for resistant DLBCL.
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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 | 0.000 | 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".