Faculty, Staff and Student Publications

Publication Date

7-23-2024

Journal

International Journal of Molecular Sciences

DOI

10.3390/ijms25158010

PMID

39125581

PMCID

PMC11311733

PubMedCentral® Posted Date

7-23-2024

PubMedCentral® Full Text Version

Post-print

Abstract

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

Keywords

Humans, Male, Prostatic Neoplasms, Biomarkers, Tumor, Proteomics, Ion Mobility Spectrometry, Prostate-Specific Antigen, Aged, Machine Learning, Middle Aged, prostate cancer, biomarker, serum, proteomics, mass spectrometry, diagnosis, prostate cancer, biomarker, serum, proteomics, mass spectrometry, diagnosis

Published Open-Access

yes

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