Hyaluronan Constitutively Regulates Activation of Multiple Receptor Tyrosine Kinases in Epithelial and Carcinoma Cells
Bibliographic record
Abstract
Hyaluronan (HA) is enriched in the pericellular matrices of many malignant human tumors, and manipulations of HA interactions have strong effects on tumor progression in animal models. Increased HA production stimulates ERBB2 activation, leading to increased cell survival activities and several malignant cell properties. On the other hand, inhibition of constitutive HA-tumor cell interactions in malignant cells inhibits these properties. We have now investigated the role of HA in activation of several additional receptor tyrosine kinases (RTKs), i.e. IGF1R-beta, PDGFR-beta, EGFR and c-MET, in colon, prostate, and breast carcinoma cells. In each case we show that antagonists of endogenous HA interactions inhibit their tyrosine phosphorylation, i.e. activation. On the other hand, we show that these RTKs are activated in phenotypically normal or relatively benign tumor cells by experimentally increasing HA production. We also investigated the role of HA in constitutive versus ligand-induced activation of RTKs. In HCA7 colon and C4-2 prostate carcinoma cells, ERBB2 is constitutively activated in a ligand-independent manner, whereas IGF1R-beta and PDGFR-beta require ligand interaction for activation. We show that both constitutive activation of ERBB2 and ligand-mediated activation of IGF1R-beta and PDGFR-beta are reversed by co-treatment of the cells with a HA antagonist. We conclude that HA serves a general function in RTK activation.
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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".