GLUT1 expression in high-risk prostate cancer: Correlation with 18F-FDG-PET/CT and clinical outcome.
Bibliographic record
Abstract
291 Background: Tumour FDG-uptake is of prognostic value in high-risk and metastatic prostate cancer (PCa). The aim of this study is to investigate the underlying glucose metabolism mechanisms of 18F-FDG-uptake on PET/CT imaging in PCa. Methods: Retrospective analysis was conducted for 94 patients diagnosed with a Gleason sum ≥8 at biopsy who underwent 18F-FDG-PET/CT imaging before radical prostatectomy. GLUT1, GLUT12 and HK2 expression were blindly scored after immunohistochemistry on radical prostatectomy specimens by 3 pathologists. 18F-FDG-uptake in primary lesion was measured by a blinded reader using maximum standardized uptake value (SUVmax). Correlations between GLUT1, GLUT12 and HK2 and SUVmax were assessed using Spearman’s rank correlation test. Survival probabilities were based on the Kaplan-Meier method. Results: With a median follow-up of 4.5 years, 56% (n=53) of patients had biochemical recurrence, 7% (n=7) progressed to castration-resistant PCa (CRPC) disease, 13% (n=12) developed metastasis and 6% (n=6) died. Correlation was found between GLUT1 expression and SUVmax level (r=0.2512, p=0.0182). In addition, SUVmax was significantly higher in tumours with high GLUT1 expression (n=17, 5.74±1.67) than tumours with low GLUT1 expression (n=71, 2.68±0.31, P=0.0037). Also, contrary to GLUT12 and HEX2 expression, a significant association was found between GLUT-1 expression levels and SUVmax index (p=0.004), lymph node status (p=0.046), volume of cancer (P=0.013), CRPC-free survival (p=0.02) and metastasis-free survival (p=0.04). Conclusions: GLUT1 expression in PCa tumours correlates with 18F-FDG-uptake and poor prognostic factors. These results suggest that this transporter is involved in the molecular mechanism of 18F-FDG-uptake in high-risk PCa and raise interest in targeting metabolic dependencies of PCa cells as a selective anticancer strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".