Lysophosphatidic acid receptor and Ral signaling in breast cancer cell migration and invasion
Bibliographic record
Abstract
Lysophosphatidic acid (LPA) is a simple lipid molecule that mediates a variety of processes such as cell survival and growth, and plays a role in the progression of a variety of cancers, stimulating cell proliferation, migration and invasion. The effects of LPA are mediated via the activity of its G protein‐coupled receptors, LPA1‐3. Recently, LPA has been shown to enhance metastasis of breast cancer to bone. However, the mechanisms by which LPA receptor signaling regulates cell migration and invasion of breast cancer cells remains unclear. Breast cancer cell proliferation has been shown to be stimulated by the small G protein Ral. Ral activity can be regulated by its guanine‐nucleotide exchange factors (RalGEFs) and the multifunctional protein β‐arrestin. We compared expression of LPA receptors, Ral, RalGDS, and β‐arrestin in the non‐tumorigenic mammary cell line MCF‐10A and the invasive breast cancer cell lines MDA‐MB‐435 and MDA‐MB‐231. Our data shows for the first time that both breast cancer cell lines have higher expression of β‐arrestin compared to MCF‐10A cells. Furthermore, MDA‐MB‐231, but not MCF‐10A cells migrate and invade in response to LPA via pertussis toxin‐sensitive G proteins and via a β‐arrestin‐Ral pathway. In summary, our data demonstrates a novel mechanism by which LPA receptors may regulate breast cancer cell migration and invasion. Research is funded by CIHR awarded to Dr. M Bhattachary
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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".