Lysophosphatidic Acid Initiates Epithelial to Mesenchymal Transition and Induces β-Catenin-mediated Transcription in Epithelial Ovarian Carcinoma
Bibliographic record
Abstract
During tumor progression, epithelial ovarian cancer (EOC) cells undergo epithelial-to-mesenchymal transition (EMT), which influences metastatic success. Mutation-dependent activation of Wnt/β-catenin signaling has been implicated in gain of mesenchymal phenotype and loss of differentiation in several solid tumors; however, similar mutations are rare in most EOC histotypes. Nevertheless, evidence for activated Wnt/β-catenin signaling in EOC has been reported, and immunohistochemical analysis of human EOC tumors demonstrates nuclear staining in all histotypes. This study addresses the hypothesis that the bioactive lipid lysophosphatidic acid (LPA), prevalent in the EOC microenvironment, functions to regulate EMT in EOC. Our results demonstrate that LPA induces loss of junctional β-catenin, stimulates clustering of β1 integrins, and enhances the conformationally active population of surface β1 integrins. Furthermore, LPA treatment initiates nuclear translocation of β-catenin and transcriptional activation of Wnt/β-catenin target genes resulting in gain of mesenchymal marker expression. Together these data suggest that LPA initiates EMT in ovarian tumors through β1-integrin-dependent activation of Wnt/β-catenin signaling, providing a novel mechanism for mutation-independent activation of this pathway in EOC 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) | 0.001 | 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".