Clinical significance of STEAP1 extracellular vesicles in prostate cancer
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EVs) are cell-derived lipid bilayer enclosed structures shed from the plasma membrane by all cell types. Evidence of EV presence in biological fluids has led to considerable efforts focused on identifying their cargo and determining their utility as a non-invasive diagnostic platform for cancer. In this study, we identify circulating STEAP1 (six-transmembrane epithelial antigen of the prostate 1)-positive EVs in the plasma of healthy males and prostate cancer patients and evaluate its diagnostic and prognostic significance. METHODS: STEAP1 was identified on EVs in prostate cancer patient plasma. EVs were validated using electron microscopy, Western blot, nanoparticle tracking analysis, and nanoscale flow cytometry. STEAP1-positive EVs were quantified for 121 males with prostate cancer and 55 healthy age-matched control males. An evaluation of STEAP1 in prostate cancer tissue was also performed using established prostate cancer cohort data (TCGA, MSKCC, and SU2C/PCF Dream Team). RESULTS: Evaluation of STEAP1-positive EVs by nanoscale flow cytometry identified a significant increase in prostate cancer patient plasma compared to healthy males. However, no association was found between total STEAP1 EV levels and disease recurrence or overall survival. Cohort data from prostate cancer tissue also found STEAP1 to be elevated in prostate cancer while no significant association with recurrence or overall survival was identified. CONCLUSIONS: STEAP1 is known to be enriched on the cells of the prostate with potential clinical significance in prostate cancer. Our results identify and quantitate STEAP1-positive EVs in plasma and provide rationale for a STEAP1 EV-based liquid biopsy as a diagnostic strategy in prostate cancer.
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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 | 0.001 | 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 | 0.000 |
| 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".