The Effect of Metformin on the Clinicopathological Features of Breast Cancer With Type 2 Diabetes
Bibliographic record
Abstract
Background: The present study aimed to review the use of hypoglycemic drugs and clinicopathological data in breast cancer patients with type 2 diabetes mellitus (T2DM), and to investigate the effect of metformin on the clinicopathological features of breast cancer in patient with T2DM.Methods: Eighty-nine patients with breast cancer hospitalized in the Second Affiliated Hospital of Xi'an Jiaotong University from January 2012 to December 2014 were included.Thirty-three patients were on metformin (metformin group) and 56 patients were on control group.Streptavidin-peroxidase (SP) method was used to quantify protein expression of molecular markers (estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2)), molecular markers of proliferation (Ki-67 and epidermal growth factor receptor (EGFR)) and epithelial-mesenchymal transition (EMT) molecular markers (matrix metalloproteinase-2 (MMP-2), E-cadherin and downstream N-cadherin).Fluorescence in situ hybridization was used to detect HER-2 (+ and ++).Results: The rate of lymph node metastasis and the level of Ki-67/ MMP-2 in the metformin group were significantly lower than those in the control group (P < 0.05).The ratio of luminal pattern in metformin group was higher than that in the control group (P < 0.05).However, there were no differences in the parameters of age, duration of diabetes, body mass index, tumor size, histological grade of cancer and clinical pathological features between the two groups.No significant difference was observed in the expressions of ER, PR, HER-2, EGFR, E-cadherin, N-cadherin and the recurrence rate between two groups.Conclusions: Metformin is associated with luminal breast cancer and can inhibit breast cancer invasion and metastasis in some cases.It may be associated with EMT and is beneficial to the prognosis of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".