GABA–GABA <sub>A</sub> Receptor Signaling Orchestrates Invasion and Metastasis in Triple Negative Breast Cancer
Bibliographic record
Abstract
ABSTRACT Cancer is a leading cause of death globally, with the majority of cancer-related deaths resulting from cancer metastasis -the process by which cancer cells disseminate to distant sites. To metastasize, cancer cells acquire traits in support of diverse cellular processes that enable dissemination, survival, and colonization. Tumor cell dissemination requires invasion at local and distant sites and this process can be influenced by intrinsic and extrinsic factors. Here, we investigate the role of the neurotransmitter gamma-aminobutyric acid (GABA) in triple-negative breast cancer (TNBC) invasion and metastasis. TNBC cells increased invasion in response to GABA and this was found to be mediated through the GABA A receptor family. TNBC cell lines were found to be responsive to exogenous GABA and also produced endogenous GABA. Pharmacological inhibition of GABA A receptors reduced TNBC invasion and cancer cell dissemination and resulted in inhibition of GSK3α activity. TNBC cell lines were found to express the GABRE subunit and loss of GABRE impaired GABA-mediated invasion and tumor cell dissemination. These findings support a role for GABA signaling through GABA A receptors in mediating TNBC 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".