Cyproheptadine inhibits in vitro and in vivo lung metastasis and drives metabolic rewiring
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for 81% of lung cancer cases, among which over 47% presented with distant metastasis at the time of diagnosis. Despite the introduction of targeted therapy and immunotherapy, enhancing the survival rate and overcoming the development of resistance remain a big challenge. Thus, it is crucial to find potential new therapeutics and targets that can mitigate lung metastasis and investigate its effects on biomarkers, such as cellular metabolomics. In the current study, we investigated the role of cyproheptadine (CPH), an FDA-approved anti-histamine drug in lung metastasis in vitro and in vivo. METHODS AND RESULTS: CPH showed potent cytotoxicity on different lung cancer cell lines in vitro. Moreover, CPH decreased invasion and migration of LLC1 and A549 cells in Matrigel invasion transwell and plate scratch assays. The in vivo LLC1 syngeneic lung cancer model found decreased number of metastatic nodules on the surface of lungs of Setd7 KO mice compared to SETD7 WT. CPH treatment resulted in decreased growth of LLC1 subcutaneous tumors compared to untreated SETD7 WT. Finally, metabolomic study of tumor tissues showed rewiring of metabolomic pathways and downregulation of amino acids, such as arginine, serine, and glycine) in Setd7 KO and WT treated with CPH compared to untreated Setd7 WT mice. CONCLUSION: These findings identify CPH as a potential therapeutic agent to block metastasis in advanced NSCLC and suggest SETD7 as a potential target for the prevention of lung metastasis.
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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) | 0.002 | 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".