Inhibition of growth and metastasis of breast carcinoma by tranilast
Bibliographic record
Abstract
1176 Tranilast (N-[3, 4 - dimethoxycin-namoyl]-anthranalic acid) is used clinically in Japan as an anti-allergic agent and to control fibrotic disorders. The anti-fibrotic effect of tranilast is thought to be mediated through the inhibition of TGF-β1 and possibly other mediators. Tranilast has also been shown to inhibit the growth of many tumor cells, such as glioma cells, uterine leiomyoma cells, oral squamous cell carcinoma and pancreatic tumor. It is well tolerated and has few adverse effects as compared to conventional anticancer drugs. In the present study, we explored the anti-tumor effect of tranilast in experimental mammary tumor, as the metastasis of breast cancer very much depends on TGF-β1. BALB/c mice were inoculated with syngenic mammary tumor cells (4T1) in mammary fat pads and tranilast was given daily by gavage. Tumor volumes were measured at various time points. After 4 weeks, mice were sacrificed and various tissues were fixed for routine histology. Our results showed that tranilast markedly reduced (> 50 %) the growth of the primary tumor in vivo. The drug also drastically reduced (> 90 %) metastases to the lungs and liver. In addition, tranilast suppressed the growth of 4T1 cells in a dose-dependent manner in culture, without any cytotoxic effect. Growth of human MDA-MB-231 breast tumor cells and other cancer cells were also inhibited by tranilast in culture. Tranilast inhibited the epithelial to mesenchymal transdifferentiation of 4T1, which is important for metastasis. Thus, these results showed that the tranilast has potential to be an effective therapeutic agent in 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) | 0.000 | 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) | 0.002 | 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".