Pathological significance and predictive value for biochemical recurrence of c‐Fes expression in prostate cancer
Bibliographic record
Abstract
BACKGROUND: c-Fes is a proto-oncogene encoded non-receptor protein-tyrosine kinase (PTK). However, genetic studies have indicated that it has anti-tumorigenic effects in certain cancers. The pathological and clinical significance of c-Fes in prostate cancer are unknown. METHODS: Expression of c-Fes was evaluated in normal glands, prostatic intraepithelial neoplasia (PIN), cancer cells in tissues of knock-in mouse adenocarcinoma prostate (KIMAP) model, and prostate cancer patients free of metastasis. Expression of c-Fes was analyzed by immunohistochemistry, and quantified by using the immunoreactivity score (IRS) (staining intensity × percentage of positive cells). Relationships between c-Fes expression and pT stage, Gleason's score (GS), and biochemical recurrence in patients who underwent radical surgery were also investigated. RESULTS: In KIMAP, the percentage in normal glands, PIN and cancer cells positive for c-Fes expression were 0 (0/7), 25.0 (2/8), and 100% (7/7), respectively. In human tissues, c-Fes expression was also significantly higher in cancer cells than in normal cells and PIN, and it correlated with pT stage (P < 0.001) and GS (P = 0.047). Multivariate analysis showed that c-Fes expression was an independent predictor of poor outcome poor prognosis (hazard ratio = 3.21, 95% confidence interval = 1.11-9.37, P = 0.032). CONCLUSION: The results suggested that c-Fes expression is a useful predictor of biochemical recurrence after radical surgery. The results also suggested that c-Fes is a potentially useful therapeutic target in prostate cancer and a predictor of biochemical recurrence after radical prostatectomy.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".