miR-137 and miR-197 Induce Apoptosis and Suppress Tumorigenicity by Targeting MCL-1 in Multiple Myeloma
Bibliographic record
Abstract
PURPOSE: Deregulation of miRNA has been implicated in the pathogenesis of multiple myeloma. We identified miR-137 and miR-197, mapped to the chromosome 1p (12)-(21) deletion region, and examined their antimyeloma activity as tumor suppressors. EXPERIMENTAL DESIGN: The expression of miR-137/197 was examined in multiple myeloma and normal plasma cells by qRT-PCR. Functional effect of miR-137/197 was analyzed by cell viability, apoptosis, clonogenic, and migration assays. Antimyeloma activity of miR-137/197 was further evaluated in vivo by lentiviral-based or lipid-based delivery in a mouse xenograft model of multiple myeloma. RESULTS: miR-137/197 expression was significantly lower in multiple myeloma cell lines and multiple myeloma patient samples compared with normal plasma cells. Transfection of miR-137/197 resulted in reduction of MCL-1 protein expression, as well as alteration of apoptosis-related genes, and induction of apoptosis, inhibition of viability, colony formation, and migration in multiple myeloma cells. MCL-1 was further validated as a direct target of miR-137/197. Conversely, overexpression of MCL-1 partially reverted the effect of miR-137/197. Importantly, in vivo lentiviral-mediated or intratumor delivery of miR-137/197 induced regression of tumors in murine xenograft models of multiple myeloma. CONCLUSIONS: Our study reveals a novel role of miR-137/197 as tumor suppressors in mediating apoptosis in multiple myeloma cells by targeting MCL-1. Our findings provide a proof-of-principle that lentivirus-based or formulated synthetic miR-137/197 exerts therapeutic activity in preclinical models, and support a framework for development of miR-137/197-based treatment strategies in patients with multiple myeloma.
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 | 0.000 |
| 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 | 0.000 |
| 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".