Cellular modulation of a G-quadruplex structure found in the lung cancer-related microRNA-3196
Bibliographic record
Abstract
RNA G-quadruplexes (G4s) are promising drug targets due to their high cellular abundance. G-rich RNA regions inherently form G4 structures, while GC-rich sequences adopt stem-loop conformations, and their dynamic equilibrium critically influences RNA function. MicroRNAs (miRs), key regulators of protein expression, undergo processing by Dicer, which specifically recognizes stem-loop structures in precursor miRs (pre-miRs). Notably, some pre-miRs containing G4-forming sequences influence Dicer cleavage, suggesting that G4s can directly regulate miR production. Moreover, pre-miRs with G4 structures present promising targets for small molecules. This research focuses on identifying and modulating G4 structure in pre-miR-3196 to restore normal lung cancer (LC) levels, offering a potential therapeutic strategy. Firstly, bioinformatic analyses indicated the presence of G4 motifs in pre-miR-3196. We then demonstrated in vitro that this RNA sequence folds into stable G4s by a combination of biophysical and biochemical assays. Then, we demonstrated the formation of these G4s in human cancer cells by confocal imaging before showing that these G4s can be modulated using the RNA G4 destabilizer PhpC, which impacts the miR-3196 biogenesis. These findings highlighted the possibility of using G4s to control the expression of mature miR-3196 and revealed the potential of using the destabilizer PhpC to adjust its G4 structure.
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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 | 0.000 |
| 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".