Knockdown of the Transforming Growth Factor-β Type III Receptor Impairs Motility and Invasion of Metastatic Cancer Cells
Bibliographic record
Abstract
The transforming growth factor-beta (TGF-beta) signaling pathway plays dual roles in epithelial cell tumorigenesis. TGF-beta is initially growth inhibitory, but as tumorigenesis progresses, TGF-beta becomes prometastatic. Although the role of the types I and II TGF-beta receptors is fairly well established, the role of the ubiquitously expressed TGF-beta type III receptor (TbetaRIII) in tumorigenesis is less defined. To examine the role of TbetaRIII in breast cancer cells, we stably expressed short hairpin RNAs specific to TbetaRIII in MDA-231 human breast cancer cells and mouse mammary carcinoma cells expressing the polyomavirus middle T oncogene (PMTLuc). MDA-231 and PMTLuc cells with down-regulated TbetaRIII expression (231-kd; PMTLuc-kd) exhibited decreased growth rate, motility, and invasion into Matrigel, as well as an increase in apoptosis, compared with control cells. MDA-231 xenografts established in nude mice metastasized, whereas tumors made by 231-kd cells did not. Nuclear factor-kappaB (NF-kappaB) activity, which is known to regulate cell growth and motility, was lower in the MDA-231 and PMTLuc knockdown cells compared with control cells. Transfection of an expression vector encoding constitutively active IKK2 into the 231-kd cells restored the ability of TbetaRIII-deficient cells to invade Matrigel and decreased their basal level of apoptosis. These data indicate that TbetaRIII differentially regulates cell growth, motility, and invasion in tumorigenic MDA-231 and PMTLuc cells and that these growth changes occur through the modulation of NF-kappaB activity.
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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 | 0.001 |
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".