Establishment of a Serum Tumor Marker for Preclinical Trials of Mouse Prostate Cancer Models
Bibliographic record
Abstract
Current prostate cancer research in both basic and preclinical trial studies employ genetically engineered mouse models. However, unlike in human prostate cancer patients, rodents have no counterpart of prostatic-specific antigen (PSA) for monitoring prostate cancer initiation and progression. In this study, we established a mouse serum tumor marker from a mouse homologue of human prostate secretory protein of 94 amino acids (PSP94). Immunohistochemistry studies on different histologic grades from both transgenic and knock-in mouse prostate cancer models showed the down-regulation of tissue PSP94 expression (P < 0.001), the same as for PSA and PSP94 in humans. The presence of mouse serum PSP94 was shown by affinity column and immunoprecipitation purification using a polyclonal mouse PSP94 antibody. A competitive ELISA protocol was established to quantify serum PSP94 levels with a sensitivity of 1 ng/mL. Quantified serum levels of mouse PSP94 ranged from 49.84 ng/mL in wild-type mice to 113.86, 400.45, and 930.90 ng/mL in mouse prostatic intraepithelial neoplasia with microinvasion, well differentiated, moderately differentiated, and poorly differentiated prostate cancer genetically engineered prostate cancer mice, respectively (P < 0.01, n = 68). This increase in serum PSP94 is also well correlated with age and tumor weight. Through longitudinal monitoring of serum PSP94 levels of castrated mice (androgen ablation therapy), we found a correlation between responsiveness/refractory prostate tissues and serum PSP94 levels. The utility of mouse serum PSP94 as a marker in hormone therapy was further confirmed by three-dimensional ultrasound imaging. The establishment of the first rodent prostate cancer serum biomarker will greatly facilitate both basic and preclinical research on human 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".