Keratinocyte Growth Factor/Fibroblast Growth Factor-7-regulated Cell Migration and Invasion through Activation of NF-κB Transcription Factors
Bibliographic record
Abstract
Keratinocyte growth factor (KGF)/fibroblast growth factor-7 (FGF-7) is a paracrine- and epithelium-specific growth factor produced by cells of mesenchymal origin. It acts exclusively through FGF-7 receptor (FGFR2/IIIb), which is expressed predominantly by epithelial cells, but not by fibroblasts, suggesting that it might function as a paracrine mediator of mesenchymal-epithelial interactions. KGF/FGF-7 plays an essential role in the growth of epithelial cells and is frequently overexpressed in cancers of epithelial origin such as pancreatic cancer, switching paracrine stimulation of KGF/FGF-7 to an autocrine loop. Less is known, however, about the signaling pathways by which KGF/FGF-7 regulates the response of epithelial cells. To delineate the signaling pathways activated by KGF/FGF-7 and examine cellular response to KGF/FGF-7 stimulation, we performed functional analysis of KGF/FGF-7 action. In this report, we show that KGF/FGF-7 activated nuclear factor kappaB (NF-kappaB), which in turn induced expression of VEGF, MMP-9, and urokinase-type plasminogen activator and increased migration and invasion of KGF/FGF-7-stimulated human pancreatic ductal epithelial cells. Expression of phosphorylation-defective IkappaBalpha (IkappaBalphaS32A,S36A), which blocked NF-kappaB activation, inhibited KGF/FGF-7-induced gene expression and cell migration and invasion. Our results demonstrate for the first time that KGF/FGF-7 induces NF-kappaB activation and that NF-kappaB plays an essential role in regulation of KGF/FGF-7-inducible gene expression and KGF/FGF-7-initiated cellular responses. Thus, these findings identify one signaling pathway for KGF/FGF-7-regulated cell migration and invasion and suggest that paracrine sources of KGF/FGF-7 are one of the malignancy-contributing factors from tumor stroma.
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".