Granulin-epithelin precursor interacts with heparan sulfate on liver cancer cells
Bibliographic record
Abstract
Granulin-epithelin precursor (GEP) is a pluripotent secretory growth factor which promotes cancer progression in a number of human cancers. However, how cancer cells interact with GEP remains unknown. In this study, we aimed to identify the cell surface-binding partner of GEP on liver cancer cells. Human recombinant GEP (rGEP) was expressed and purified to homogeneity. The rGEP was shown to trigger phosphorylation of AKT and ERK1/2 in liver cancer cells. We demonstrated cell surface attachment of rGEP, which was blocked by prebinding of platelet-derived growth factor-AA, platelet-derived growth factor-BB and fibroblast growth factor-2. Therefore, heparan sulfate (HS) had been reasoned as the binding partner of rGEP. Heparinase digestion validated the role of HS on supporting the attachment. The heparin-binding domain of GEP was mapped to RRH(555-557) in the C-terminal region. Suppression of the HS polymerase exostosin-1 reduced the rGEP binding and rGEP-mediated signaling transduction. Suppression of a specific HS proteoglycan, glypican-3, also showed a partial reduction of rGEP binding and an inhibition on rGEP-mediated activation of AKT. Furthermore, glypican-3 was shown to correlate with the expressions of GEP in clinical samples (Spearman's ρ = 0.363, P = 0.001). This study identified HS, partly through glypican-3, as a novel binding partner of GEP on the surface of liver cancer cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".