Identification of Serum Amyloid A as a Biomarker to Distinguish Prostate Cancer Patients with Bone Lesions
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Prostate cancer has a propensity to metastasize to the bone. Currently, there are no curative treatments for this stage of the disease. Sensitive biomarkers that can be monitored in the blood to indicate the presence or development of bone metastases and/or response to therapies are lacking. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is an affinity-based approach that allows sensitive and high-throughput protein profiling and screening of biological samples. METHODS: We used SELDI-TOF MS for protein profiling of sera from prostate cancer patients (n = 38) with and without bone metastases in our effort to identify individual or multiple serum markers that may be of added benefit to those in current use. Serum was applied to ProteinChip surfaces (H4 and IMAC) to quickly screen samples and detect peaks predominating in the samples obtained from patients with bone metastases. Unique proteins in the bone metastasis cohort observed by SELDI-TOF MS were identified by two-dimensional gel electrophoresis, in-gel trypsin digestion, and tandem MS. The identities of the proteins were confirmed by ELISA and immunodepletion assays. RESULTS: The cluster of unique proteins in the sera of patients with bone metastases was identified as isoforms of serum amyloid A. Machine-learning algorithms were also used to identify patients with bone metastases with a sensitivity and specificity of 89.5%. CONCLUSIONS: SELDI-TOF MS protein profiling in combination with other proteomic approaches may provide diagnostic tools with potential clinical applications and serve as tools to aid in the discovery of biomarkers associated with various diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it