Gap Junctions Enhance the Antiproliferative Effect of MicroRNA‐124‐3p in Glioblastoma Cells
Bibliographic record
Abstract
MicroRNA (miRNA) holds promise as a novel therapeutic tool for cancer treatment. However, the transfection efficiency of current delivery systems represents a bottleneck for clinical applications. Here, we demonstrate that gap junctions mediate an augmentative effect on the antiproliferation mediated by miR-124-3p in U87 and C6 glioblastoma cells. The functional inhibition of gap junctions using either siRNA or pharmacological inhibition eliminated the miR-124-3p-mediated antiproliferation, whereas the enhancement of gap junctions with retinoic acid treatment augmented this miR-124-3p-mediated antiproliferation. A similar effect was observed in glioblastoma xenograft models. More importantly, patch clamp and co-culture assays demonstrated the transmission of miR-124-3p through gap junction channels into adjacent cells. In further exploring the impact of gap junction-mediated transport of miR-124-3p on miR-124-3p target pathways, we found that miR-124-3p inhibited glioblastoma cell growth in part by decreasing the protein expression of cyclin-dependent kinase 6, leading to cell cycle arrest at the G0 /G1 phase; moreover, pharmacological regulation of gap junctions affected this cell cycle arrest. In conclusion, our results indicate that the "bystander" effects of functional gap junctions composed of connexin 43 enhance the antitumor effect of miR-124-3p in glioblastoma cells by transferring miR-124-3p to adjacent cells, thereby enhancing G0 /G1 cell cycle arrest. These observations provide a new guiding strategy for the clinical application of miRNA therapy in tumor treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".