Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer
Bibliographic record
Abstract
Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H 2 O 2 and a mediator. We obtained an unprecedented 0.3 fg mL –1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83–91% clinical sensitivity and 78–96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.
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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 | 0.000 |
| 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 | 0.000 |
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".