Data from Nm23-H1 Suppresses Metastasis by Inhibiting Expression of the Lysophosphatidic Acid Receptor <i>EDG2</i>
Bibliographic record
Abstract
Abstract Nm23-H1 transcriptionally down-regulates expression of the lysophosphatidic acid receptor EDG2 and this down-regulation is critical for Nm23-H1–mediated motility suppression in vitro. We investigated the effect of altered EDG2 expression on Nm23-H1–mediated metastasis suppression in vivo. Clonal MDA-MB-435–derived tumor cell lines transfected with Nm23-H1 together with either a vector control or EDG2 had similar anchorage-dependent and anchorage-independent growth rates in vitro. However, a 45- and 300-fold inhibition of motility and invasion (P < 0.0001), respectively, was observed in Nm23-H1/vector lines, whereas coexpression of EDG2 restored activity to levels observed in the parental line. Using fluorescently labeled cells and ex vivo microscopy, the capacity of these cells to adhere, arrest, extravasate, and survive in the murine lung over a 24-h time course was measured. Only 5% of Nm23-H1/vector–transfected cells were retained in the murine lung 6 h following tail vein injection; coexpression of EDG2 enhanced retention 8- to 13-fold (P < 0.01). In a spontaneous metastasis assay, the primary tumor size of Nm23-H1/vector and Nm23-H1/EDG2 clones was not significantly different. However, restoration of EDG2 expression augmented the incidence of pulmonary metastasis from 51.9% to 90.4% (P = 2.4 × 10−5), comparable with parental MDA-MB-435 cells. To determine the relevance of this model system to human breast cancer, a cohort of breast carcinomas was stained for Nm23-H1 and EDG2 and a statistically significant inverse correlation between these two proteins was revealed (r = −0.73; P = 0.004). The data indicate that Nm23-H1 down-regulation of EDG2 is functionally important to suppression of tumor metastasis. [Cancer Res 2007;67(24):11751–9]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".