Invasiveness of breast cancer cells MDA‐MB‐231 through extracellular matrix is increased by the estradiol metabolite 4‐hydroxyestradiol
Bibliographic record
Abstract
In malignant breast cancer, estrogen metabolism is altered, favoring the accumulation of hydroxyestradiols, which can generate free radicals. These reactive species can activate matrix metalloproteinases (MMPs), which in turn can hydrolyze the proteins of the extracellular matrix (ECM) that act as a barrier to tumor cell passage. The aim of this study was to determine whether reactive oxygen species generated by 4-hydroxyestradiol (4-OHE(2)) can activate MMP-2 and then enhance the invasiveness of breast cancer cells MDA-MB-231 in vitro. Enzymatic assay and gel zymography demonstrated that 4-OHE(2) at a concentration as low as 10(-8) M led to the conversion of proMMP-2 to active MMP-2. Activation of proMMP-2 by 4-OHE(2) was inhibited by the Cu,Zn-SOD supporting the involvement of the free radical superoxide anion (O(2)(*-)). Using invasion chambers coated with matrigel (artificial ECM), 4-OHE(2) (10(-8) M) enhanced the invasiveness of MDA-MB-231 breast cancer cells by 3-fold. The addition of Cu,Zn-SOD reduced the invasiveness of MDA-MB-231 cells by more than 2-fold, supporting the involvement of O(2)(*-) generated by 4-OHE(2). Addition of an MMP-2 inhibitor completely inhibited the enhancement of invasiveness induced by 4-OHE(2), which demonstrates the importance of activating MMP-2 by 4-OHE(2). On the other hand, estradiol, which does not have a catechol structure, did not generate free radicals, and it could not activate proMMP-2 or enhance the invasiveness of beast cancer cells. Although these data need to be confirmed in an animal model, this study suggests that the accumulation of 4-OHE(2) in breast tumors could enhance the invasiveness of breast cancer cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".