Exosomal miRNAs in the Tumor Microenvironment of Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is a malignancy of plasma cells in the bone marrow and is characterized by the clonal proliferation of B-cells producing defective monoclonal immunoglobulins. Despite the latest developments in treatment, drug resistance remains one of the major challenges in the therapy of MM. The crosstalk between MM cells and other components within the bone marrow microenvironment (BME) is the major determinant of disease phenotypes. Exosomes have emerged as the critical drivers of this crosstalk by allowing the delivery of informational cargo comprising multiple components from miniature peptides to nucleic acids. Such material transfers have now been shown to perpetuate drug-resistance development and disease progression in MM. MicroRNAs(miRNAs) specifically play a crucial role in this communication considering their small size that allows them to be readily packed within the exosomes and widespread potency that impacts the developmental trajectory of the disease inside the tumor microenvironment (TME). In this review, we aim to provide an overview of the current understanding of the role of exosomal miRNAs in the epigenetic modifications inside the TME and its pathogenic influence on the developmental phenotypes and prognosis of MM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".