Hierarchical clustering of immunohistochemical analysis of the activated ErbB/PI3K/Akt/NF-κB signalling pathway and prognostic significance in prostate cancer
Bibliographic record
Abstract
BACKGROUND: The PI3K/Akt signalling pathway, induced by epidermal growth factor receptor (EGFR) and Her-2, is involved in the constitutive activation of NF-kappaB in prostate cancer cell lines. In this study, we extended the in vitro observation using an ex vivo model of prostate cancer tissues and assessed the prognostic significance of the PI3K/Ak/NF-kappaB signalling determinants. METHODS: We analysed a prostate cancer tissue microarray of 63 patients for the expression of total and activated EGFR, Her-2 receptors and the signalling molecules PTEN, phospho-PTEN, Akt, phospho-Akt and the NF-kappaB subunit p65. Data were analysed using Spearman's rho test, Kaplan-Meier curves and multivariate Cox regression analysis. In addition, a non-supervised hierarchical clustering analysis was applied to stratify patients according to prognostic groups in terms of risk of recurrence. RESULTS: The concomitant overexpression of activated EGFR and Her-2 was correlated with the nuclear expression of NF-kappaB. EGFR, phospho-EGFR, phospho-Her-2, ErbB3 and nuclear NF-kappaB were associated with the overall biochemical recurrence (BCR) of patients. The non-supervised hierarchical clustering analysis resulted in the separation of patients into five groups according to BCR. CONCLUSIONS: These results validate the previous in vitro data on ErbB involvement in NF-kappaB activation and shows evidence for a significant role of ErbB/PI3K/Akt/NF-kappaB signalling in the progression of prostate 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".