A Systematic Review of MicroRNAs in Nasal NK/T-Cell Lymphoma: Diagnostic, Prognostic, and Therapeutic Perspectives
Bibliographic record
Abstract
Nasal NK/T-cell lymphoma (NKTCL) is a highly aggressive malignancy with significant predominance with Epstein-Barr virus infection. MicroRNAs (miRNAs) have been found to be key regulators in cancer biology that influence tumorigenesis, disease progression, and immune evasion. This systematic review examines the role of miRNAs in diagnostics, prognosis, and treatment of NKTCL. PubMed and SCOPUS databases were systematically searched using the keywords "miRNAs" and "nasal lymphoma." Duplicate removal and selection based on the inclusion criteria produced 15 studies for final inclusion. Study design, specific miRNAs under study, and their relevance to NKTCL have been extracted for this narrative synthesis. PRISMA guidelines were followed, and results were narratively synthesized. In the selected studies, miRNAs showed their utility as potential diagnostic markers. Downregulated miRNAs, including miR-15a, miR-101, and miR-342-3p, differentiated NKTCL from normal tissue, while EBV-encoded miRNAs, including miR-BART20-5p and miR-BART8, were identified as potential circulating biomarkers. Prognostically, miRNAs such as miR-223 and miR-342-3p were associated with poor survival and aggressive disease features. Therapeutically, miRNA-based interventions targeting EBV miRNAs, such as miR-BART20-5p and miR-BART9, were emphasized for their ability to modulate immune pathways and oncogenic signaling. Despite these promising reports, heterogeneity in study designs and geographic settings limits their generalization. miRNAs are emerging as key players in the treatment of NKTCL, with application in diagnosis, prognosis, and therapy. Larger, heterogeneous cohorts and the progression of miRNA-based therapeutic research should further validate such studies. This review highlights the potential of miRNA in translation into better outcomes in NKTCL patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".