Spatiotemporal regulation of the hepatocyte growth factor receptor MET activity by sorting nexins 1/2 in HCT116 colorectal cancer cells
Bibliographic record
Abstract
Cumulative research findings support the idea that endocytic trafficking is crucial in regulating receptor signaling and associated diseases. Specifically, strong evidence points to the involvement of sorting nexins (SNXs), particularly SNX1 and SNX2, in the signaling and trafficking of the receptor tyrosine kinase (RTK) MET in colorectal cancer (CRC). Activation of hepatocyte growth factor (HGF) receptor MET is a key driver of CRC progression. In the present study, we utilized human HCT116 CRC cells with SNX1 and SNX2 genes knocked out to demonstrate that their absence leads to a delay in MET entering early endosomes. This delay results in increased phosphorylation of both MET and AKT upon HGF stimulation, while ERK1/2 (extracellular signal-regulated kinases 1 and 2) phosphorylation remains unaffected. Despite these changes, HGF-induced cell proliferation, scattering, and migration remain similar between the parental and the SNX1/2 knockout cells. However, in the absence of SNX1 and SNX2, these cells exhibit increased resistance to TRAIL-induced apoptosis. This research underscores the intricate relationship between intracellular trafficking, receptor signaling, and cellular responses and demonstrates for the first time that the modulation of MET trafficking by SNX1 and SNX2 is critical for receptor signaling that may exacerbate the disease.
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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".