Expression and Signaling Activity of Wnt-5a/Discoidin Domain Receptor-1 and Syk Plays Distinct but Decisive Roles in Breast Cancer Patient Survival
Bibliographic record
Abstract
PURPOSE: The loss of Wnt-5a, a G-protein-coupled receptor ligand, or Syk, an intracellular kinase, has in separate studies been associated with poor prognosis of breast cancer patients. Both proteins are involved in cell adhesion, a key event in epithelial cancer metastasis. Here, we have investigated whether Syk is part of the Wnt-5a/discoidin domain receptor-1 (DDR1) signaling pathway and if a signaling interaction of these proteins is important for breast cancer-specific survival. EXPERIMENTAL DESIGN: The signaling interactions between Wnt-5a/DDR1 and Syk were addressed in mammary cell lines. Their mRNA and protein levels and the respective clinical correlates were investigated in 94 cases of primary breast cancer. RESULTS: The expression of Wnt-5a and Syk correlated in four of five tumor cell lines. However, despite a constitutive association between Syk and the Wnt-5a-dependent adhesion receptor DDR1, we found no evidence of a Wnt-5a/DDR1-mediated activation of Syk. Instead, beta(1) integrins initiate the adhesion-induced activation of Syk. In tumors from breast cancer patients, the protein expression of Wnt-5a and Syk were differently regulated at the translational and transcriptional level, respectively. Analysis of breast cancer-specific survival revealed that the presence of Wnt-5a and Syk in primary tumors has good predictive value for a favorable outcome. Intriguingly, a simultaneous loss of both proteins did not reduce survival more than loss of either. CONCLUSIONS: Despite the difference in regulation of Wnt-5a and Syk protein expression and their lack of signaling interaction, our clinical data indicate that a favorable prognosis in breast cancer requires the expression and signaling activity of both.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".