Metabolite profiling reveals a connection between aldehyde dehydrogenase 1A3 and GABA metabolism in breast cancer metastasis
Bibliographic record
Abstract
INTRODUCTION: Aldehyde dehydrogenase 1A3 (ALDH1A3) is a cancer stem cell (CSC) marker and in breast cancer it is associated with triple-negative/basal-like subtypes and aggressive disease. Studies on the mechanisms of ALDH1A3 in cancer have primarily focused on gene expression changes induced by the enzyme; however, its effects on metabolism have thus far been unstudied and may reveal novel mechanisms of pathogenesis. OBJECTIVE: Determine how ALDH1A3 alters the metabolite profile in breast cancer cells and assess potential impacts. METHOD: Triple-negative MDA-MB-231 tumors and cells with manipulated ALDH1A3 levels were assessed by HPLC-MS metabolomics and metabolite data was integrated with transcriptome data. Mice harboring MDA-MB-231 tumors with or without altered ALDH1A3 expression were treated with γ-aminobutyric acid (GABA) or placebo. Effects on tumor growth, and lungs and brain metastasis were quantified by staining of fixed thin sections and quantitative PCR. Breast cancer patient datasets from TCGA, METABRIC and GEO were used to assess the co-expression of GABA pathway genes with ALDH1A3. RESULTS: Integrated metabolomic and transcriptome data identified GABA metabolism as a primary dysregulated pathway in ALDH1A3 expressing breast tumors. Both ALDH1A3 and GABA treatment enhanced metastasis. Patient dataset analyses revealed expression association between ALDH1A3 and GABA pathway genes and corresponding increased risk of metastasis. CONCLUSION: This study revealed a novel pathway affected by ALDH1A3, GABA metabolism. Like ALDH1A3 expression, GABA treatment promotes metastasis. Given the clinical use of GABA mimics to relieve chemotherapy-induced peripheral nerve pain, further study of the effects of GABA in breast cancer progression is warranted.
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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 | 0.001 |
| 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".