microRNA sequencing for biomarker detection in the diagnosis, classification and prognosis of Diffuse Large B Cell Lymphoma
Bibliographic record
Abstract
Despite being considered a single disease, Diffuse Large B Cell Lymphoma (DLBCL) presents with variable backgrounds, which results in heterogeneous outcomes among patients, with 40% of them still having primary refractory disease or relapse. Thus, novel biomarkers are needed. In addition, multiple factors regarding its pathogenesis remain unclear. In this context, recent investigations point to the relevance of microRNAs (miRNAs) in cancer. However, regarding DLBCL, there is inconsistency in the data reported. Therefore, in this work, the main goals were to determine a miRNA set with utility as biomarkers for DLBCL diagnosis, classification, prognosis and treatment response, as well as to decipher the mechanism of action of deregulated miRNAs in the origin of the disease. We analyzed miRNA expression in a cohort of 78 DLBCL patients and 17 controls using small RNA sequencing and performed a miRNA-mRNA interaction network analysis. This way, we were able to define new miRNA expression signatures for diagnosis, classification, treatment response and prognosis, and we identified plausible mechanisms of action by which deregulated miRNAs could be involved in DLBCL pathogenesis. In summary, our study remarks that miRNAs could play an important role in DLBCL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".