Tranilast inhibits the growth and metastasis of mammary carcinoma
Bibliographic record
Abstract
Tranilast (N-[3,4-dimethoxycinnamonyl]-anthranilic acid) is a drug of low toxicity that is orally administered, and has been used clinically in Japan as an antiallergic and antifibrotic agent. Its antifibrotic effect is thought to depend on the inhibition of transforming growth factor-beta (TGF-beta). It has also been shown to exert antitumor effects, but its mode of action is unclear. Here, we explored the antitumor effects of tranilast in vitro and in vivo. Tranilast inhibited the proliferation of several tumor cell lines including mouse mammary carcinoma (4T1), rat mammary carcinoma stem cell (LA7), and human breast carcinoma (MDA-MB-231 and MCF-7). Tranilast blocked cell-cycle progression in vitro. In the highly metastatic 4T1 cell line, tranilast inhibited phospho-Smad2 generation, consistent with a blockade of TGF-beta signaling. It also inhibited the activation of MAP kinases (extracellularly regulated kinase 1 and 2 and JNK), which have been linked to TGF-beta-dependent epithelial-to-mesenchymal transition and, indeed, it blocked epithelial-to-mesenchymal transition. Although tranilast only partially inhibited TGF-beta production by 4T1 tumor cells, it potently inhibited the production of TGF-beta, interferon-gamma, IL-6, IL-10, and IL-17 by lymphoid cells, suggesting a general anti-inflammatory activity. In vivo, female BALB/c mice were inoculated with syngeneic 4T1 cells in mammary fat pads and treated with tranilast by gavage. Tranilast reduced (>50%) the growth of the primary tumor. However, its effects on metastasis were more striking, with more than 90% reduction of metastases in the lungs and no metastasis in the liver. Thus, tranilast has potential activity as an antimetastatic 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) | 0.001 | 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 | 0.001 |
| 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".