Transforming Growth Factor-β1, Transforming Growth Factor-β2, and Transforming Growth Factor-β3 Enhance Ovarian Cancer Metastatic Potential by Inducing a Smad3-Dependent Epithelial-to-Mesenchymal Transition
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) is thought to play a role in the pathobiological progression of ovarian cancer because this peptide hormone is overexpressed in cancer tissue, plasma, and peritoneal fluid. In the current study, we investigated the role of the TGF-beta/Smad3 pathway in ovarian cancer metastasis by regulation of an epithelial-to-mesenchymal transition. When cancer cells were cultured on plastic, TGF-beta1, TGF-beta2, and TGF-beta3 induced pro-matrix metalloproteinase (MMP) secretion, loss of cell-cell junctions, down-regulation of E-cadherin, up-regulation of N-cadherin, and acquisition of a fibroblastoid phenotype, consistent with an epithelial-to-mesenchymal transition. Furthermore, Smad3 small interfering RNA transfection inhibited TGF-beta-mediated changes to a fibroblastic morphology, but not MMP secretion. When cancer cells were cultured on a three-dimensional collagen matrix, TGF-beta1, TGF-beta2, and TGF-beta3 stimulated both pro-MMP and active MMP secretion and invasion. Smad3 small interfering RNA transfection of cells cultured on a collagen matrix abrogated TGF-beta-stimulated invasion and MMP secretion. Analysis of Smad3 nuclear expression in microarrays of serous benign tumors, borderline tumors, and cystadenocarcinoma revealed that Smad3 expression could be used to distinguish benign and borderline tumors from carcinoma (P = 0.006). Higher Smad3 expression also correlated with poor survival (P = 0.031). Furthermore, a direct relationship exists between Smad3 nuclear expression and expression of the mesenchymal marker N-cadherin in cancer patients (P = 0.0057). Collectively, these results implicate an important role for the TGF-beta/Smad3 pathway in mediating ovarian oncogenesis by enhancing metastatic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".