ILKAP Promotes the Metastasis of Hepatocellular Carcinoma Cells by Inhibiting β‐Catenin Degradation and Enhancing the WNT Signaling Pathway
Bibliographic record
Abstract
The incidence of Hepatocellular carcinoma (HCC) and HCC-related deaths have remarkably increased over the recent decades. It has been reported that β-catenin activation can be frequently observed in HCC cases. This study identified the integrin-linked kinase-associated phosphatase (ILKAP) as a novel β-catenin-interacting protein. ILKAP is localized both in the nucleus and cytoplasm and regulates the WNT pathway in different ways. First, it is demonstrated that ILKAP activates the WNT pathway in HCC cells by increasing the protein level of β-catenin and other proteins associated with the WNT signaling, such as c-Myc and CyclinD1. Next, it is shown that ILKAP promotes the metastasis of HCC both in vitro and in vivo in a zebrafish xenograft model. It is also found that ILKAP dephosphorylates the GSK3β and CK1, contributing to the reduced ubiquitination of β-catenin. Furthermore, it is identified that ILKAP functions by mediating binding between TCF4 and β-catenin to enhance expression of WNT target genes. Taken together, the study demonstrates a critical function of ILKAP in metastasis of HCC, since ILKAP is crucial for the activation of the WNT pathway via stabilization of β-catenin and increased binding between TCF4 and β-catenin.
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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) | 0.002 | 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".