Regulation of DNA Methylation in Human Breast Cancer
Bibliographic record
Abstract
Urokinase-type plasminogen activator (uPA) is a member of the serine protease family and can break down various components of the extracellular matrix to promote growth, invasion, and metastasis of several malignancies including breast cancer. In the current study we examined the role that the DNA methylation machinery might be playing in regulating differential uPA gene expression in breast cancer cell lines. uPA mRNA is expressed in the highly invasive, hormone-insensitive human breast cancer cell line MDA-MB-231 but not in hormone-responsive cell line MCF-7. Using methylation-sensitive PCR, we show that 90% of CpG dinucleotides in the uPA promoter are methylated in MCF-7 cells, whereas fully demethylated CpGs were detected in MDA-MB-231 cells. uPA promoter activity, which is directly regulated by the Ets-1 transcription factor, is inhibited by methylation as determined by uPA promoter-luciferase reporter assays. We then tested whether the state of expression and methylation of the uPA promoter correlates with the global level of DNA methyltransferase and demethylase activities in these cell lines. We show that maintenance DNA methyltransferase activity is significantly higher in MCF-7 cells than in MDA-MB-231 cells, whereas demethylase activity is higher in MDA-MB-231 cells. We suggest that the combination of increased DNA methyltransferase activity with reduced demethylase activity contributes to the methylation and silencing of uPA expression in MCF-7 cells. The converse is true in MDA-MB-231 cells, which represents a late stage highly invasive breast cancer. The histone deacetylase inhibitor, Trichostatin A, induces the expression of the uPA gene in MDA-MB-231 cells but not in MCF-7 cells. This supports the hypothesis that DNA methylation is the dominant mechanism involved in the silencing of uPA gene expression. Taken together, these results provide insight into the mechanism regulating the transcription of the uPA gene in the complex multistep process of breast cancer progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".