Cysteine- rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients’ prognosis
Bibliographic record
Abstract
Cysteine- rich secretory protein 3 (CRISP3) prognostic significance in prostate cancer (PCA) has generated mixed result. Herein, we investigated and independently validated CRISP3 expression in relation to ERG and PTEN genomic aberrations and clinical outcome. CRISP3 protein expression was examined by immunohistochemistry using a cohort of patients with localized PCA (n = 215) and castration resistant PCA (CRPC) (n = 46). The Memorial Sloan Kettering (MSKCC) and Swedish cohorts were used for prognostic validation. Results showed, CRISP3 protein intensity to be significantly associated with neoplastic epithelium, being highest in CRPC vs. benign prostate tissue (p < 0.0001), but was not related to Gleason score (GS). CRISP3 mRNA was significantly associated with higher GS (p = 0.022 in MSKCC, p = 1.1e-4 in Swedish). Significant association between CRISP3 expression and clinical outcome was documented at the mRNA but not the protein expression levels. CRISP3 mRNA expression was related to biochemical recurrence in the MSKCC (p = 0.038) and lethal disease in the Swedish cohort (p = 0.0086) and retained its prognostic value in the subgroup of patients with GS 6 & 7. Furthermore, CRISP3 protein and mRNA expression was significantly associated with positive ERG status and with PTEN deletions. Functional biology analysis documented phenylalanine metabolism as the most significant pathway governing high CRISP3 and ERG expression in this subtype of PCA. In conclusion, the combined status of CRISP3, ERG and PTEN define a molecular subtype of PCA with poorest and lethal outcome. Assessing their combined value may be of added value in stratifying patients into different prognostic groups and identify those with poorest clinical outcome.
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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) | 0.001 | 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".