Topical mevastatin promotes wound healing by inhibiting the transcription factor c-Myc via the glucocorticoid receptor and the long non-coding RNA Gas5
Bibliographic record
Abstract
Diabetic foot ulcers (DFUs), a life-threatening complication of diabetes mellitus, have limited treatment options, often resulting in amputations. HMG-CoA reductase inhibitors such as statins are cholesterol-reducing agents that may provide a new therapeutic option. Statins target the cholesterol pathway and block the synthesis of the wound-healing inhibitors farnesyl pyrophosphate (FPP) and cortisol, ligands for the glucocorticoid receptor (GR). Here we demonstrate that the naturally occurring statin mevastatin reverses FPP's effects and promotes healing by using in vitro wound healing assays, human ex vivo and porcine in vivo wound models, and DFU tissue. Moreover, we measured cortisol levels by ELISA and found that mevastatin inhibited cortisol synthesis in keratinocytes and biopsies from patients with DFU. Of note, topical mevastatin stimulated epithelialization and angiogenesis in vivo . Mevastatin also reversed FPP-mediated induction of the GR target, the transcription factor c-Myc (a biomarker of non-healing wounds), in porcine and human wound models. Importantly, mevastatin reversed c-Myc overexpression in DFUs. It induced expression of the long noncoding RNA Gas5 that blocks c-Myc expression, which was confirmed by overexpression studies. We conclude that topical mevastatin accelerates wound closure by promoting epithelialization via multiple mechanisms: modulation of GR ligands and induction of the long noncoding RNA Gas5, leading to c-Myc inhibition. In light of these findings, we propose that repurposing statin drugs for topical treatment of DFUs may offer another option for managing this serious condition.
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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".