Discovery of Candidate Tumor Markers for Prostate Cancer via Proteomic Analysis of Cell Culture–Conditioned Medium
Bibliographic record
Abstract
OBJECTIVE: Prostate-specific antigen measurement, widely used for early detection of prostate cancer (CaP), suffers from low specificity. Additional tumor markers are needed for the early detection of clinically relevant CaP. Our objective was to perform a qualitative proteomic analysis of conditioned medium (CM) from the CaP cell line PC3(AR)(6). METHODS: We used a roller bottle culture system to culture the PC3(AR)(6) cell line in chemically defined serum-free medium for 14 days. By using strong anion-exchange chromatography, we fractionated the CM and trypsinized the fractions. The tryptic peptides were further fractionated by reversed-phase C-18 chromatography before being subjected to electrospray ionization tandem mass spectrometry. We used MASCOT software to search the mass spectra generated and organized identified proteins based on their genome ontology classification of cellular location. We used an immunoassay to measure a newly identified secreted protein, Mac-2BP, and kallikreins 5, 6, and 11 in serum samples from CaP patients and healthy men. RESULTS: We classified 262 proteins according to cellular location; the sample was found to contain a significant proportion of secreted (23%) and membrane (16%) proteins. In a proportion of cancer patients compared with healthy men, we determined by ELISA that serum concentrations of a novel candidate biomarker Mac-2BP were increased. CONCLUSIONS: These identified proteins, and possibly many others found in the CM, may have utility as novel CaP biomarkers.
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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 | 0.001 |
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".