Expression of Concern: Up‐regulation of Wnt‐1 and <i>β</i>‐catenin production in patients with advanced metastatic prostate carcinoma
Bibliographic record
Abstract
BACKGROUND: Wnt-1 and beta-catenin expression levels play an important role in several malignancies. The authors determined the level of production of Wnt-1 and beta-catenin in normal and malignant human prostate carcinoma cell lines. Surgical pathology specimens from primary prostatic adenocarcinoma, lymph node metastases, and skeletal metastases were used to establish a correlation between the level of Wnt-1/beta-catenin expression, Gleason score, serum prostate-specific antigen (PSA) status, and androgen receptor (AR) status. METHODS: Immunohistochemical analysis was used to investigate the expression of Wnt-1 and beta-catenin in human prostate carcinoma cell lines and in paraffin embedded sections of archival samples from 67 patients with prostate carcinoma. Comparison was made with the expression of tumoral AR and lymph node and skeletal metastases. These results were used to establish a correlation with the clinicopathologic status of patients with prostate carcinoma. RESULTS: Levels of both Wnt-1 and beta-catenin were low in normal prostate cells and were expressed highly in human prostate carcinoma cell lines. Wnt-1 and cytoplasmic/nuclear beta-catenin expression was observed in 52% and 34%, respectively, of primary prostate carcinoma specimens. High levels of expression of Wnt-1 and beta-catenin were seen in 77% of lymph node metastases and in 85% of skeletal metastases. These increased levels of expression were related directly to the Gleason score and to serum PSA levels in these patients. Maximum levels of Wnt-1 and beta-catenin production were observed in skeletal metastases, whereas normal prostatic tissue failed to exhibit any detectable nuclear staining for beta-catenin. CONCLUSIONS: High levels of Wnt-1 and beta-catenin expression were associated with advanced, metastatic, hormone-refractory prostate carcinoma, in which they can serve as markers of disease progression.
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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 | 0.001 |
| 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) | 0.001 | 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".