Inhibitory Impact of the Amino Benzoic Derivative DAB-2-28 on the Process of Epithelial–Mesenchymal Transition in Human Breast Cancer Cells
Bibliographic record
Abstract
Macrophage-mediated inflammation is known to be involved in the epithelial–mesenchymal transition (EMT) of various types of cancer. This makes macrophage-derived inflammatory factors prime targets for the development of new treatments. This study uncovers the therapeutic potential and action mechanism of DAB-2-28, a small-molecule derived from para-aminobenzoic acid, in the treatment of breast cancer. The luminal MCF-7 and the triple-negative MDA-MB-231 cancer cell lines used in this study represent, respectively, breast cancers in which the differentiation states are related to the epithelial phenotype of the mammary gland and breast cancers expressing a highly aggressive mesenchymal phenotype. In MCF-7 cells, soluble factors from macrophage-conditioned media (CM-MØ) induce a characteristic morphology of mesenchymal cells with an upregulated expression of Snail1, a mesenchymal marker, as opposed to a decrease in the expression of E-cadherin, an epithelial marker. DAB-2-28 does not affect the differential expression of Snail1 and E-cadherin in response to CM-MØ, but negatively impacts other hallmarks of EMT by decreasing invasion and migration capacities, in addition to MMP9 expression and gelatinase activity, in both MCF-7 and MDA-MB-231 cells. Moreover, DAB-2-28 inhibits the phosphorylation of key pro-EMT transcriptional factors, such as NFκB, STAT3, SMAD2, CREB, and/or AKT proteins, in breast cancer cells exposed to different EMT inducers. Overall, our study provides evidence suggesting that inhibition of EMT initiation or maintenance is a key mechanism by which DAB-2-28 can exert anti-tumoral effects in 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".