The Antitumor Effect of C-terminus of Hsp70-Interacting Protein via Degradation of c-Met in Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The mesenchymal-epithelial transition factor (MET) receptor can be overexpressed in solid tumors, including small cell lung cancer (SCLC). However, the molecular mechanism regulating MET stability and turnover in SCLC remains undefined. One potential mechanism of MET regulation involves the C-terminus of Hsp70-interacting protein (CHIP), which targets heat shock protein 90-interacting proteins for ubiquitination and proteasomal degradation. In the present study, we investigated the functional effects of CHIP expression on MET regulation and the control of SCLC cell apoptosis and invasion. METHODS: proto-oncogene), we examined the expression pattern of c-Met and CHIP in SCLC cell lines by western blotting. To investigate whether CHIP overexpression reduced cell proliferation and invasive activity in SCLC cell lines, we transfected cells with CHIP and performed a cell viability assay and cellular apoptosis assays. RESULTS: We found an inverse relationship between the expression of CHIP and MET in SCLC cell lines (n=5). CHIP destabilized the endogenous MET receptor in SCLC cell lines, indicating an essential role for CHIP in the regulation of MET degradation. In addition, CHIP inhibited MET-dependent pathways, and invasion, cell growth, and apoptosis were reduced by CHIP overexpression in SCLC cell lines. CONCLUSION: CHIP is capable of regulating SCLC cell apoptosis and invasion by inhibiting MET-mediated cytoskeletal and cell survival pathways in NCI-H69 cells. CHIP suppresses MET-dependent signaling, and regulates MET-mediated SCLC motility.
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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 | 0.000 |
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".