Aldehyde dehydrogenase 1A3 influences breast cancer progression via differential retinoic acid signaling
Bibliographic record
Abstract
Aldehyde dehydrogenase (ALDH) 1A enzymes produce retinoic acid (RA), a transcription induction molecule. To investigate if ALDH1A1 or ALDH1A3-mediated RA signaling has an active role in breast cancer tumorigenesis, we performed gene expression and tumor xenograft studies. Analysis of breast patient tumors revealed that high levels of ALDH1A3 correlated with expression of RA-inducible genes with retinoic acid response elements (RAREs), poorer patient survival and triple-negative breast cancers. This suggests a potential link between ALDH1A3 expression and RA signaling especially in aggressive and/or triple-negative breast cancers. In MDA-MB-231, MDA-MB-468 and MDA-MB-435 cells, ALDH1A3 and RA increased expression of RA-inducible genes. Interestingly, ALDH1A3 had opposing effects in tumor xenografts, increasing tumor growth and metastasis of MDA-MB-231 and MDA-MB-435 cells, but decreasing tumor growth of MDA-MB-468 cells. Exogenous RA replaced ALDH1A3 in inducing the same opposing tumor growth and metastasis effects, suggesting that ALDH1A3 mediates these effects by promoting RA signaling. Genome expression analysis revealed that ALDH1A3 induced largely divergent gene expression in MDA-MB-231 and MDA-MB-468 cells which likely resulted in the opposing tumor growth effects. Treatment with DNA methylation inhibitor 5-aza-2'deoxycytidine restored uniform RA-inducibility of RARE-containing HOXA1 and MUC4 in MDA-MB-231 and MDA-MB-468 cells, suggesting that differences in epigenetic modifications contribute to differential ALDH1A3/RA-induced gene expression in breast cancer. In summary, ALDH1A3 induces differential RA signaling in breast cancer cells which affects the rate of breast cancer progression.
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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".