MicroRNAs in the anti-cancer effects of Ginsenosides: A Systematic Review
Bibliographic record
Abstract
This systematic review highlights the pivotal functions of ginsenosides in cancer treatment through miRNA regulation. Ginsenosides, bioactive herbal compounds derived from ginseng, exhibit significant anti-cancer properties through mechanisms including inhibition of cell proliferation, epithelial-to-mesenchymal transition (EMT), metastasis, invasion, and induction of autophagy and apoptosis. MicroRNAs (miRNAs), small non-coding RNAs, play critical roles in gene regulation and have emerged as potential diagnostic, prognostic, and therapeutic targets in various cancers. Ginsenosides influence miRNA expression, underexpressing oncogenic miRNAs and overexpressing tumor suppressor miRNAs, thereby exerting their anti-cancer effects. The literature review covered studies from 2011 to 2021 sourced from PubMed, Scopus, Cochrane Library, and Web of Science, adhering to the PRISMA guidelines. Eligible studies were screened, resulting in the selection of 26 preclinical studies. These studies demonstrate that ginsenosides modulate the expression of various miRNAs, contributing to anti-tumorigenic activities across different cancer types, including glioma, non-small cell lung cancer, breast cancer, acute leukemia, hepatocellular carcinoma, ovarian cancer, medulloblastoma, prostate cancer, liver cancer, oral squamous cell carcinoma, retinoblastoma, and gallbladder cancer. By influencing miRNA pathways, ginsenosides can inhibit tumor growth, migration, invasion, and induce apoptosis, highlighting their therapeutic potential in oncology.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".