Citral reduces breast tumor growth by inhibiting the cancer stem cell marker ALDH1A3
Bibliographic record
Abstract
Breast cancer stem cells (CSCs) can be identified by increased Aldefluor fluorescence caused by increased expression of aldehyde dehydrogenase 1A3 (ALDH1A3), as well as ALDH1A1 and ALDH2. In addition to being a CSC marker, ALDH1A3 regulates gene expression via retinoic acid (RA) signaling and plays a key role in the progression and chemotherapy resistance of cancer. Therefore, ALDH1A3 represents a druggable anti-cancer target of interest. Since to date, there are no characterized ALDH1A3 isoform inhibitors, drugs that were previously described as inhibiting the activity of other ALDH isoforms were tested for anti-ALDH1A3 activity. Twelve drugs (3-hydroxy-dl-kynurenine, benomyl, citral, chloral hydrate, cyanamide, daidzin, DEAB, disulfiram, gossypol, kynurenic acid, molinate, and pargyline) were compared for their efficacy in inducing apoptosis and reducing ALDH1A3, ALDH1A1 and ALDH2-associated Aldefluor fluorescence in breast cancer cells. Citral was identified as the best inhibitor of ALDH1A3, reducing the Aldefluor fluorescence in breast cancer cell lines and in a patient-derived tumor xenograft. Nanoparticle encapsulated citral specifically reduced the enhanced tumor growth of MDA-MB-231 cells overexpressing ALDH1A3. To determine the potential mechanisms of citral-mediated tumor growth inhibition, we performed cell proliferation, clonogenic, and gene expression assays. Citral reduced ALDH1A3-mediated colony formation and expression of ALDH1A3-inducible genes. In conclusion, citral is an effective ALDH1A3 inhibitor and is able to block ALDH1A3-mediated breast tumor growth, potentially via blocking its colony forming and gene expression regulation activity. The promise of ALDH1A3 inhibitors as adjuvant therapies for patients with tumors that have a large population of high-ALDH1A3 CSCs is discussed.
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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".