WDR4 promotes colorectal cancer progression by activating the GSK3β/β-catenin pathway
Bibliographic record
Abstract
WD repeat domain 4 (WDR4) has been reported to promote tumor metastasis in various cancers. However, its precise function in colorectal cancer (CRC) has not been reported yet. Herein, the expression pattern of WDR4 in CRC was determined by analyzing Gene Expression Omnibus datasets (GSE110225, GSE127069, GSE156355, and GSE184093) and GEPIA online dataset. In vitro and in vivo experiments, including CCK-8, colony formation, flow cytometry, wound healing, transwell assays, and xenograft mouse models, were used to investigate the role of WDR4 in CRC. Firstly, data from Kaplan-Meier database showed that high expression of WDR4 was associated with the poor prognosis of CRC patients. Then, upregulation of WDR4 was confirmed in clinical CRC tissues. In vitro functional experiments suggested that overexpression of WDR4 promoted cell proliferation, migration, and invasion, while knockdown of WDR4 has the opposite effects. Also, the oncogenic role of WDR4 was also verified in in vivo experiments. CO-IP-LC/MS analysis uncovered that glycogen synthase kinase 3β (GSK3β) is the central protein that binds to WDR4. Mechanistically, WDR4 activated the β-catenin pathway by promoting GSK3β phosphorylation. This study demonstrates that WDR4 promotes CRC progression through activating GSK3β/β-catenin pathway, indicating that WDR4 might be a potential therapeutic target for CRC treatment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".