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October 15, 2025Open Access

Urinary miRNA Profiles with Machine Learning for Noninvasive Detection and Prognosis of Urological Malignancies

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Authors

YBYuto BabaSIShun IwasaYYYota Yasumizu

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Overview

Observational analysis identifies urinary miRNA profiles for cancer detection and prognosis in diverse cohorts, suggesting promising diagnostics.

Key Points

  • Machine learning algorithms distinguish urological cancers from healthy samples, achieving AUCs of 0.92 for RCC, 0.92 for PCa, and 0.96 for UC.
  • A panel of urinary miRNAs showed high discriminatory power for recurrence-free survival with time-dependent AUCs ranging from 0.75 to 0.89 across cancer types.
  • Prospective analysis included 419 urine samples from renal cell carcinoma, prostate cancer, and urothelial carcinoma, comparing with healthy individuals.
  • These findings highlight the utility of miRNA signatures as noninvasive diagnostic and prognostic tools for urological malignancies.

Cite This Study

Baba et al. (2025) studied this question.

synapsesocial.com/papers/68f01110f081da0584b566e4https://doi.org/10.1101/2025.10.11.25336309
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