Suppressing miRNA-15a/-16 expression by interleukin-6 enhances drug-resistance in myeloma cells
Bibliographic record
Abstract
The bone marrow microenvironment facilitates the survival, differentiation, and proliferation of myeloma (MM) cells. This study identified that microRNA-15a and -16 expressions tightly correlated with proliferation and drug sensitivity of MM cells. miRNA-15a/-16 expression in MM cells was significantly increased after treatment with cytotoxic agents. The interaction of bone marrow stromal cells (BMSC) with MM cells resulted in decreased miRNA-15a/-16 expression and promoted the survival of the MM cells. Interleukin-6 (IL-6) produced by BMSCs suppressed the expression of miRNA-15a and 16 in a time- and dose- dependent pattern, with the suppression on miRNA-15a being more significant than on miRNA-16. miRNA-15a-transfected MM cells were found to be arrested in G1/S checkpoint, and the transfected MM cells had decreased growth and survival. In conclusion, our data suggest that via suppressing miRNA-15a and -16 expressions, IL-6 secreted by BMSCs promotes drug-resistance in myeloma cells.
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 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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".