Urine-Based Noninvasive Detection of Prostate Cancer Using Human Olfactory Receptor-Embedded Nanodiscs

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Accuracy 0.890 (89.0%)

• Primary Outcome: Prostate cancer detection accuracy using OR-ND sensor array

• Nnt Or Sensitivity: AUC 0.964; sensitivity and specificity not reported

• Confidence Interval: Not reported

Clinical Application

Limited - requires sophisticated nanotechnology platform and machine learning infrastructure not currently available in routine clinical practice Would require TGA approval as novel diagnostic device; potential to reduce unnecessary biopsies could align with PBS cost-effectiveness requirements, but technology readiness unclear Men undergoing prostate cancer screening, particularly those with elevated PSA requiring further evaluation

Abstract

The diagnosis of prostate cancer (PCa) is limited by the low specificity of prostate-specific antigen (PSA) testing, which contributes to overdiagnosis and patient exposure to unnecessary invasive prostate biopsies. Here, we report a urine-based diagnostic platform that combines a six-member human olfactory receptor-embedded nanodisc (OR-ND) sensor array with fluorescence sensing and machine learning to detect PCa-associated volatile organic compounds (VOCs). Six ORs were reconstituted into lipid nanodiscs and evaluated using urine samples from a strictly curated subcohort of 40 PCa patients and 33 healthy controls (n = 73) to establish baseline sensor responsiveness. Subsequently, to ensure diagnostic robustness, machine learning classifiers were developed and cross-validated using an expanded data set of 290 samples, achieving an accuracy of 0.890 and an AUC of 0.964. Analysis of the full receptor data set revealed distinct response patterns, and statistical screening with correlation filtering identified OR2W1, OR51E1, and OR51E2 as the most informative features. Using these selected receptors, a random forest classifier achieved an accuracy of 0.890 and an AUC of 0.964, demonstrating high sensitivity and specificity. OR response patterns showed a closer association with Gleason score than with serum PSA levels, indicating that urinary VOC signatures capture tumor-related metabolic information complementary to conventional biomarkers. These results demonstrate that an OR-ND sensor array coupled with machine learning enables accurate and noninvasive detection and classification of PCa from urine samples. Our study provides a modular framework extensible to other VOC-associated diseases.

References

1. 1.Yoo, J., Kim, Y., Kang, K., Ahn, Y. J., Bang, S., Koo, K. C., & Park, T. H. (2026). Urine-based noninvasive detection of prostate cancer using human olfactory receptor-embedded nanodiscs. ACS Sensors. https://doi.org/10.1021/acssensors.6c00794