Prostate-Specific Membrane Antigen (PSMA) Expression Predicts Need for Early Treatment in Prostate Cancer Patients Managed with Active Surveillance
Bibliographic record
Abstract
Metabolic dysregulation is an early event in carcinogenesis. Here, we examined the expression of enzymes involved in de novo lipogenesis (ATP-citrate lyase: ACLY), glucose uptake (Glucose Transporter 1: GLUT1), and folate–glutamate metabolism (Prostate-Specific Membrane Antigen: PSMA) as potential biomarkers of risk for early prostate cancer progression. Patients who were managed initially on active surveillance with a Gleason score of 6 or a low-volume Gleason score of 7 (3 + 4) were accrued from a prostate cancer diagnostic assessment program. Patients were asked to donate their baseline diagnostic biopsy tissues and permit access to their clinical data. PSMA, GLUT1, and ACLY expression were examined with immunohistochemistry (IHC) in baseline biopsies, quantitated by Histologic Score for expression in benign and malignant glands, and compared with patient time remaining on active surveillance (time-on-AS). All three markers showed trends for elevated expression in malignant compared to benign glands, which was statistically significant for ACLY. On univariate analysis, increased PSMA and GLUT1 expression in malignant glands was associated with shorter time-on-AS (HR: 5.06, [CI 95%: 1.83–13.94] and HR: 2.44, [CI 95%: 1.10–5.44], respectively). Malignant ACLY and benign gland PSMA and GLUT1 expression showed non-significant trends for such association. On multivariate analysis, overexpression of PSMA in malignant glands was an independent predictor of early PC progression (p = 0.006). This work suggests that the expression of metabolic enzymes determined by IHC on baseline diagnostic prostate biopsies may have value as biomarkers of risk for rapid PC progression. PSMA may be an independent predictor of risk for progression and should be investigated further in systematic studies.
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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 | 0.000 | 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 | 0.001 |
| 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".