Mechanism of Action of Resveratrol Affecting the Biological Function of Breast Cancer Through the Glycolytic Pathway
Bibliographic record
Abstract
Background: Phosphoglycerate kinase 1 (PGK1) plays a crucial role in the glycolytic pathway and its overexpression has a negative impact on tumor development and prognosis. Resveratrol, a natural polyphenolic compound, has gained significant attention in recent years due to its anti-inflammatory, antioxidant, and anti-tumor properties. However, the mechanism by which resveratrol inhibits breast cancer growth, invasion, and metastasis through the PGK1 glycolytic pathway is still not fully understood. Methods: We used the Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas database to analyze the expression levels of glycolytic enzymes in different breast tissues and their correlation with the prognosis of breast cancer patients. The effect of resveratrol on the biological functions of breast cancer was studied through wound healing experiments and Transwell migration and invasion experiments. Reverse transcription quantitative polymerase chain reaction (RT-qPCR), Western blot, and in vivo mouse tumorigenesis experiments were used to explore the possible molecular mechanism of resveratrol inhibiting the occurrence and development of breast cancer. Results: Resveratrol exerted oncogenic effects both in vivo and in vitro. In our study, we provided additional evidence to support the role of resveratrol in breast cancer treatment. Specifically, we found that resveratrol effectively reduced the expression of PGK1 in BT-549 cells. This reduction is achieved by regulating an important transcription factor c-Myc. As a result, the cellular glycolytic pathway is blocked, leading to the inhibition of malignant biological behavior in breast cancer cells. Conclusion: Our findings suggest that targeting the PGK1 glycolytic pathway could be a promising approach for resveratrol-based treatment 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".