Expression analysis of E-cadherin, Slug and GSK3β in invasive ductal carcinoma of breast
Bibliographic record
Abstract
BACKGROUND: Cancer progression is linked to a partially dedifferentiated epithelial cell phenotype. The signaling pathways Wnt, Hedgehog, TGF-beta and Notch have been implicated in experimental and developmental epithelial mesenchymal transition (EMT). Recent findings from our laboratory confirm that active Wnt/beta-catenin signaling is critically involved in invasive ductal carcinomas (IDCs) of breast. METHODS: In the current study, we analyzed the expression patterns and relationships between the key Wnt/beta-catenin signaling components- E-cadherin, Slug and GSK3beta in IDCs of breast. RESULTS: Of the 98 IDCs analyzed, 53 (54%) showed loss/or reduced membranous staining of E-cadherin in tumor cells. Nuclear accumulation of Slug was observed in 33 (34%) IDCs examined. Loss or reduced level of cytoplasmic GSK3beta expression was observed in 52/98 (53%) cases; while 34/98 (35%) tumors showed nuclear accumulation of GSK3beta. Statistical analysis revealed associations of nuclear Slug expression with loss of membranous E-cadherin (p = 0.001); nuclear beta-catenin (p = 0.001), and cytoplasmic beta-catenin (p = 0.005), suggesting Slug mediated E-cadherin suppression via the activation of Wnt/beta-catenin signaling pathway in IDCs. Our study also demonstrated significant correlation between GSK3beta nuclear localization and tumor grade (p = 0.02), suggesting its association with tumor progression. CONCLUSION: The present study for the first time provided the clinical evidence in support of Wnt/beta-catenin signaling upregulation in IDCs and key components of this pathway - E-cadherin, Slug and GSK3beta with beta-catenin in implementing EMT in these 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".