Inhibition of autotaxin production or activity blocks lysophosphatidylcholine‐induced migration of human breast cancer and melanoma cells
Bibliographic record
Abstract
Increased expression of autotaxin in tumors including glioblastoma, breast, renal, ovarian, lung, and thyroid cancers is associated with increased tumor aggressiveness. Autotaxin promotes metastasis as well as cell growth, survival, and migration of cancer cells. These actions could depend on the noncatalytic effects of autotaxin on cell adhesion, or the catalytic activity of autotaxin, which converts lysophosphatidylcholine into lysophosphatidate in the extracellular fluid surrounding the tumor. Both lysophosphatidylcholine (LPC) and lysophosphatidate have been reported to stimulate migration through their respective G-protein coupled receptors. The present study determines the roles of autotaxin, LPC, and lysophosphatidate in controlling the migration of two cancer cell lines: MDA-MB-231 breast cancer cells, which produce little autotaxin and MDA-MB-435 melanoma cells that secrete significant levels of autotaxin. LPC alone was unable to stimulate the migration of either cell type unless autotaxin was present. Knocking down autotaxin secretion, or inhibiting its catalytic activity, blocked cell migration by preventing lysophosphatidate production and the subsequent activation of LPA(1/3) receptors. We conclude that inhibiting autotaxin production or activity could provide a beneficial adjuvant to chemotherapy for preventing tumor growth and metastasis in patients with high autotaxin expression in their tumors.
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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) | 0.001 | 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".