A New Role for an Old Drug: Metformin Targets Micro<scp>RNA</scp>s in Treating Diabetes and Cancer
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a family of short, noncoding, 19-23 base pair RNA molecules. Due to their unique role in gene regulation in various tissues, miRNAs play important roles in regulating insulin secretion, metabolic disease, and cancer biology. Emerging evidence demonstrates that miRNAs could also be novel diagnostic markers for a variety of disease states. Additionally, miRNAs have been found to function either as oncogenes, or tumor suppressor genes in cerian cancers. An increasing number of studies have been conducted investigating new drugs targeting miRNAs as a potential anticancer therapy. Metformin is the most widely prescribed medication for treating Type 2 diabetes (T2D). Recent clinical data suggests that metformin impacts the miRNA profile in T2D subjects. Most excitingly, studies have found that metformin is protective against cancer. The anticancer activity of metformin is mediated through a direct regulation of miRNAs, which further modulates several downstream genes in metabolic or preoncogenic pathways. These miRNAs are, therefore, prospective therapeutic targets for treating diabetes and cancer which is the topic of this review. Further study on the regulation of miRNAs by metformin could result in novel therapeutic strategies for recurrent or drug-esistant cancer, and as part of combinatorial approaches with conventional anticancer therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".