Co-overexpression of Met and Hepatocyte Growth Factor Promotes Systemic Metastasis in NCI-H460 Non-Small Cell Lung Carcinoma Cells
Bibliographic record
Abstract
Complete resection of early-stage non-small cell lung cancer (NSCLC) is potentially curative, yet approximately 50% of patients are at risk for developing metastatic recurrence. Met, the receptor for hepatocyte growth factor (HGF) is a receptor tyrosine kinase with demonstrated roles in regulating cellular proliferation, motility, morphogenesis, and apoptosis. Met receptor and its ligand, HGF, are commonly overexpressed in NSCLC, and their overexpression has been associated with poor prognosis, which could potentially involve a paracrine and/or autocrine activation loop. However, there is as yet no direct evidence that HGF-Met signaling directly promotes metastasis in NSCLC cells. Using retroviral transduction, we overexpressed the human c-met and hgf complementary DNA, alone or in combination in the NCI-H460 human large cell carcinoma cell line. The HGF/Met co-overexpressing (H460-HGF/Met) cells demonstrated enhanced tumorigenicity in xenograft SCID mice. When these cells are implanted orthotopically into the lungs of nude rats, only the H460-HGF/Met cells showed higher spontaneous metastases to distant organs including bone, brain, and kidney. These results provide evidence that autocrine overactivation of the Met- HGF loop enhances systemic metastases in NSCLC. Targeted interference of this loop may potentially be an effective adjuvant therapy to improve survival of early-stage NSCLC patients.
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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".