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 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".