Expression of ERBB Family Members as Predictive Markers of Prostate Cancer Progression and Mortality
Bibliographic record
Abstract
Background: EGFR, ERBB2, ERBB3, and ERBB4 are growth receptors of the ERBB family implicated in the development of epithelial cancers. Studies have suggested a role for EGFR and ERBB3 in the development of prostate cancer (PC), while the involvement of ERBB2 and ERBB4 remains unclear. In this study, we evaluated the expression of all members of the ERBB family in PC tissue from a large cohort and determined their contribution, alone or in combination, as prognostic markers. Methods: Using immunofluorescence coupled with digital image analyses, we quantified the expression of EGFR, ERBB2, ERBB3, and ERBB4 on radical prostatectomy specimens (n = 285) arrayed on six tissue microarrays. By combining EGFR, ERBB2, and ERBB3 protein expression in a decision tree model, we identified an association with biochemical recurrence (log rank = 25.295, p < 0.001), development of bone metastases (log rank = 23.228, p < 0.001), and cancer-specific mortality (log rank = 24.586, p < 0.001). Conclusions: Our study revealed that specific protein expression patterns of ERBB family members are associated with an increased risk of PC progression and mortality.
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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 | 0.001 |
| 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".