WIF1, a Wnt pathway inhibitor, regulates SKP2 and c-myc expression leading to G1 arrest and growth inhibition of human invasive urinary bladder cancer cells
Bibliographic record
Abstract
Epigenetic silencing of secreted wingless-type (Wnt) antagonists through hypermethylation is associated with tobacco smoking and with invasive bladder cancer. The secreted Wnt inhibitory factor-1 (WIF1) has shown consistent growth-inhibitory effect on various cancer cell lines. Therefore, we assessed the mechanisms of action of WIF1 by either restoring WIF1 expression in invasive bladder cancer cell lines (T24 and TSU-PR1) or using a recombinant protein containing functional WIF1 domain. Both ectopic expression of WIF1 and treatment with WIF1 domain protein resulted in cell growth inhibition via G(1) arrest. The G(1) arrest induced by WIF1 is associated with down-regulation of SKP2 and c-myc and up-regulation of p21/WAF1 and p27/Kip1. Conversely, reexpression of SKP2 in WIF1-overexpressing TSU-PR1 cells attenuated the WIF1-induced G(1) arrest. Furthermore, inhibition of nuclear Wnt signaling by either dominant-negative LEF1 or short hairpin RNA of TCF4 also reduced SKP2 expression. The human SKP2 gene contains two TCF/LEF1 consensus binding sites within the promoter. Chromatin immunoprecipitation/real-time PCR analysis revealed that both WIF1 and dominant-negative LEF1 expression decreased the in vivo binding of TCF4 and beta-catenin to the SKP2 promoter. Together, our results suggest that mechanisms of WIF1-induced G(1) arrest include (a) SKP2 down-regulation leading to p27/Kip1 accumulation and (b) c-myc down-regulation releasing p21/WAF1 transcription. Additionally, we show that WIF1 inhibits in vivo bladder tumor growth in nude mice. These observations suggest a mechanism for transformation of bladder epithelium on loss of WIF1 function and provide new targets such as SKP2 for intervention in WIF1-deficient bladder cancer.
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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".