A New Application for the Drug Tranilast: Effects on Breast Cancer Cell Proliferation, Migration, and Invasion
Bibliographic record
Abstract
Background Tranilast (Rizaben ® ) has been used to treat allergic and auto‐immune diseases for the last 26 years. The objective of this study was to investigate whether tranilast also has an effect on breast cancer cell biological features relevant to cancer progression, based on the its mechanism of action which involves TGFβsignaling. Methods Cultures of the BT‐474 human breast cancer cell line were used. Cell proliferation, clonogenicity, cell migration, endoglin and MMP‐9 expression were assessed by: MTT assay, soft agar colony formation, wound assay and immunocytochemistry respectively. Results Tranilast was found to inhibit breast cancer cell proliferation by 70%. In addition, the drug decreased by 54% the number and size of cell colonies grown in soft agar, consistent with a decrease in clonogenicity. The closure of wounds made in cancer cell monolayers was slowed (55%) by Tranilast, consistent with an inhibitory effect on tumor cell migration. Endoglin, which plays a role in TGFβ signaling, was downregulated by the drug. MMP‐9, which is a metalloproteinase involved in tumor invasion and metastasis was also decreased by tranilast. Conclusions These results indicate that tranilast exerts inhibitory effects on key biological features of human breast cancer cells. The results are consistent with an anti‐tumor effect which can be exploited in preclinical and clinical therapeutic trials.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".