Lifestyle Factors and MicroRNAs: A New Paradigm in Cancer Chemoprevention
Bibliographic record
Abstract
MicroRNAs (miRNAs) are characterized as small RNA molecules that modulate gene transcription in a posttranslational manner. Functionally, miRNAs play important roles in a diverse number of biological processes, including cell development, differentiation, proliferation, and apoptosis. Consequently, changes in the expression pattern of miRNAs have been associated with multiple human pathologies, including cancer. Based on these alterations, distinct miRNAs can be utilized as markers for cancer risk evaluation or used in tumour detection. Recent evidence has indicated that lifestyle factors, such as nutrition, physical activity, and glycemic control provide health benefits through regulation of miRNA expression. In this review, we provide a concise overview of miRNA regulation, biosynthesis, and their expression patterns in normal and malignant tissue. We then summarize the emerging knowledge of how lifestyle factors, including nutrients, exercise, and hypoglycemic agents modify miRNAs and are involved in cancer prevention. Finally, we conclude by providing recommendation for future investigations into novel agents that can modulate miRNAs and act as chemotherapeutic agents against cancer.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".