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September 21, 2025Open Access

Non-Invasive Diagnostic Evaluation of Urinary Exosomal Let-7c Cluster Expression in Bladder Cancer Using Machine Learning Approaches

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Authors

SKSukhad KuralLKLalit KumarAPAbhay Kumar Pathak

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Overview

Analysis reveals improved diagnostic performance of urinary miRNAs in bladder cancer, suggesting machine learning models enhance diagnostic capabilities.

Key Points

  • The machine learning model achieved an accuracy of 80.0% for distinguishing bladder cancer patients from healthy controls.
  • Urinary exosomal miR-99a-5p and miR-125b-5p demonstrated significant diagnostic performance with AU-ROC scores of 0.71 and 0.64 respectively.
  • Integration of clinical variables with miRNA expression improved diagnostic accuracy to 73.3% for high-versus low-grade differentiation.
  • Bioinformatics confirmed the relevance of urinary miRNAs in bladder cancer-related pathways such as PI3K-Akt and p53.

Cite This Study

Kural et al. (2025) studied this question.

synapsesocial.com/papers/68d43afa713b0b5dfea7aba4https://doi.org/10.1101/2025.09.18.677033
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