The role of hepatocyte growth factor (scatter factor) in epithelialmesenchymal transition and breast cancer
Bibliographic record
Abstract
North American women have a one in eight lifetime risk of developing breast cancer, and approximately one in three women with breast cancer will die of metastases. We, and others, have recently shown that high levels of expression of hepatocyte growth factor (HGF) and its receptor Met are associated with invasive human breast cancer and may be causally linked to metastasis. This high level of HGF and Met expression has been considered as a possible indicator of earlier recurrence and shortened survival in breast cancer patients. In contrast, HGF expression (but not Met) is strongly suppressed in normal breast epithelial cells. HGF and Met are therefore candidate targets for therapeutic intervention in the treatment of breast cancer. We have recently demonstrated that sustained activation or hyper-activation of c-Src and Stat3, which occurs in invasive breast cancer, can stimulate strong expression of HGF in carcinoma cells. In contrast, transient induction of Stat3 occurs in normal epithelium and promotes mammary tubulogenesis. We hypothesize that increased autocrine HGF-Met signaling is a critical downstream function of c-Src-Stat3 activation in mammary tumorigenesis. Future studies will identify novel Stat3 consensus sites that regulate HGF promoter activity and HGF expression preferentially in carcinoma cells and could lead to novel therapeutic drugs that specifically block HGF expression in mammary carcinoma cells, and which could be used in combined treatments to abrogate metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".