Ectopic expression of BIRC5-targeting miR-101-3p overcomes bone marrow stroma-mediated drug resistance in multiple myeloma cells
Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM) cells gain protection against drugs through interaction with bone marrow stromal cells (BMSCs). This form of resistance largely accounts for resistance to therapy in MM patients which warrants further exploration to identify more potential therapeutic targets. METHODS: -BMSC co-cultures were measured using fluorescence plate reader and flowcytometry, respectively. miRNA was overexpressed in MM cell lines using Lentiviral transduction, miRNA-3'UTR binding was examined using luciferase assay. RESULTS: We found that BMSCs downregulated miR-101-3p and upregulated survivin (BIRC5) in MM cells. Survivin was downregulated by miR-101-3p overexpression and found to be a direct target of miR-101-3p using 3'UTR luciferase assay. Overexpression of survivin increased viability of MM cells in the presence of anti-myeloma drugs, and miR-101-3p inhibition by anti-miR against miR-101-3p upregulated survivin. Furthermore, overexpression of miR-101-3p or silencing of survivin triggered apoptosis in MM cells and sensitized them to anti-myeloma drugs in the presence of BMSCs overcoming the stroma-induced drug resistance. CONCLUSIONS: Our study demonstrates that BMSC-induced resistance to drugs is associated with survivin upregulation which is a direct target of miR-101-3p. This study also identifies miR-101-3p-survivin interaction as a druggable target involved in stroma-mediated drug resistance in MM and suggests it for developing more efficient therapeutic strategies.
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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) | 0.000 | 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 | 0.001 |
| 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".