Metformin May Block the Way Breast Cancer Metastasis: A Meta-analysis and Mechanism Review
Bibliographic record
Abstract
Abstract Background Metformin, which is cheap and easy to get, is a first-line anti-hyperglycemia drug. Recently, its anti-tumor effect has been revealed. Here we performed a meta-analysis to summarize previous studies and a narrative review to gather the mechanisms involved in the potential relationship. Methods We searched related articles in database of Pubmed, EMbase, Web of science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang and Sinomed and obtained 8 clinic trials that investigated the connection between metformin and breast cancer metastasis, containing 2 randomized controlled trials (RCTs) and 6 retrospective cohort studies. We evaluated each retrospective cohort study by Newcastle-Ottawa Scale (NOS), while RCT by Chcorane Risk of Bias tool. Pooled hazard ratios (HRs), risk ratios (RRs) and we calculated associated 95% confidence intervals (CIs) with a random-effect, generic inverse variance method. We also collected the possible mechanisms of cancer metastasis inhibition from metformin. Results A total of 8 studies containing 13919 breast cancer patients without distant metastasis before they got anticancer treatment. The result showed that adjuvant metformin in treatment of local breast cancer facilitated to suppress metastasis (HR = 0.69, 95% CI = 0.57–0.82, p < 0.0001, I2 = 0%), and the result was consistent with the subgroup of breast cancer patients with type 2 diabetes mellitus (T2DM) (HR = 0.68, 95% CI = 0.57–0.82, p < 0.0001, I2 = 0%). Conclusion The meta-analysis suggested metformin might repress the metastasis and be benefit to distant metastasis-free survival (DMFS) when added to systemic breast cancer therapy, supporting anti-tumor effects of metformin on breast 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".