Increased production and secretion of HGF α‐chain and an antagonistic HGF fragment in a human breast cancer progression model
Bibliographic record
Abstract
Invasive human breast carcinomas frequently coexpress increased hepatocyte growth factor (HGF) and its receptor Met, suggesting that establishment of an autocrine HGF loop is important in malignant disease. This study examines the expression patterns of HGF and Met activation during tumorigenesis and metastasis using a MCF10A-based model of Ha-Ras-induced human breast cancer progression. Deregulation of cadherin-based cell-cell adhesions, decreased expression of cytokeratins 8/18 and increased activity of matrix metalloproteinases such as MMP-2 occurs in premalignant and malignant (metastatic) cell lines compared to the parental nonmalignant cell line. Compared to the benign parent cell line, premalignant and malignant cell lines exhibit increased secretion of full length HGF alpha-chain and elevated Met tyrosine phosphorylation in complete medium. Interestingly, the premalignant and malignant cells also secrete a approximately 55 kDa HGF fragment. Epitope mapping of the approximately 55 kDa HGF fragment supports the presence of the N-terminal domain of the HGF alpha-chain with a truncation in the C-terminal domain. The approximately 55 kDa HGF fragment shows mobility in SDS-PAGE faster than HGF alpha-chain, but slightly slower than NK4, a previously established full antagonist of HGF. The separated approximately 55 kDa HGF fragment binds to animmobilized Met-IgG fusion protein, and inhibits both HGF/Met-IgG binding and HGF-induced Met-tyrosine phosphorylation. These results are the first demonstration of an antagonistic approximately 55 kDa HGF fragment secreted during breast carcinoma progression, which may have a negative regulatory effect on HGF signaling in premalignant breast epithelial cells.
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".