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Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAs.

Lea Eckhart1, Sabrina Rau2, Markus Eckstein3

  • 1Center for Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany.

Journal of Cellular and Molecular Medicine
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

Four microRNAs (miRNAs) can accurately distinguish muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). A classification model using three miRNAs aids in predicting bladder cancer invasiveness, potentially supporting clinical decisions.

Keywords:
machine learning algorithmsmicroRNAmolecular subtypesmuscle‐invasive bladder cancerpT1 high‐grade tumours

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Area of Science:

  • Oncology
  • Molecular Biology
  • Biomarker Discovery

Background:

  • Bladder cancer diagnosis relies on histopathology, with accurate staging crucial for treatment decisions.
  • Distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC) is critical for patient management.
  • MicroRNAs (miRNAs) have emerged as potential biomarkers in various cancers, including bladder cancer.

Purpose of the Study:

  • To validate the diagnostic potential of four specific miRNAs in independent bladder cancer cohorts.
  • To develop and assess accurate classification models for predicting bladder cancer invasiveness.
  • To investigate the association of miRNA expression with molecular subtypes of bladder cancer.

Main Methods:

  • miRNA expression profiling was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissues from pTa low-grade (lg), pT1 high-grade (hg), and MIBC patients.
  • A k-nearest neighbours (KNN) classifier was trained using miRNA expression data to differentiate between NMIBC (pTa lg) and MIBC.
  • Conformal prediction methods were applied to enhance the reliability of the KNN classification model.

Main Results:

  • Significant differences in the expression of miR-138-5p, miR-200a-3p, miR-146b-5p, and miR-155-5p were observed between MIBC and pTa lg bladder cancer.
  • A KNN classifier utilizing three miRNAs achieved high accuracy (0.94) in distinguishing pTa lg from MIBC.
  • The classification model demonstrated robust performance, with conformal prediction eliminating misclassifications in the test cohort. pT1 hg tumors were classified as MIBC in 32% of cases.
  • Specific miRNA expressions correlated significantly with distinct molecular subtypes of bladder cancer.

Conclusions:

  • The four studied miRNAs effectively differentiate MIBC from NMIBC.
  • A three-miRNA based classification model accurately predicts tumor invasiveness, offering potential support for histopathological diagnosis.
  • These findings may aid clinical decision-making regarding radical cystectomy versus bladder-sparing strategies, particularly for pT1 hg tumors.