β-Arrestin/Ral Signaling Regulates Lysophosphatidic Acid–Mediated Migration and Invasion of Human Breast Tumor Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The lipid mediator lysophosphatidic acid (LPA) plays a role in cancer progression and signals via specific G protein-coupled receptors, LPA(1-3). LPA has been shown to enhance the metastasis of breast carcinoma cells to bone. However, the mechanisms by which LPA receptors regulate breast cancer cell migration and invasion remain unclear. Breast cancer cell proliferation has been shown to be stimulated by Ral GTPases, a member of the Ras superfamily. Ral activity can be regulated by the multifunctional protein beta-arrestin. We now show that HS578T and MDA-MB-231 breast cancer cells and MDA-MB-435 melanoma cells have higher expression of beta-arrestin 1 mRNA compared with the nontumorigenic mammary MCF-10A cells. Moreover, we found that the mRNA levels of LPA1, LPA2, beta-arrestin 2, and Ral GTPases are elevated in the advanced stages of breast cancer. LPA stimulates the migration and invasion of MDA-MB-231 cells, but not of MCF-10A cells, and this is mediated by pertussis toxin-sensitive G proteins and LPA1. However, ectopic expression of LPA1 in MCF-10A cells caused these cells to acquire an invasive phenotype. Gene knockdown of either beta-arrestin or Ral proteins significantly impaired LPA-stimulated migration and invasion. Thus, our data show a novel role for beta-arrestin/Ral signaling in mediating LPA-induced breast cancer cell migration and invasion, two important processes in metastasis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it