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Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome.

Akshay Akshay1,2, Mustafa Besic1, Annette Kuhn3

  • 1Functional Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.

International Journal of Molecular Sciences
|February 10, 2024
PubMed
Summary

Researchers developed a machine learning approach to identify mRNA signatures for non-ulcerative Bladder Pain Syndrome (BPS). A three-gene signature (TPPP3, FAT1, NCALD) accurately classifies BPS, offering a new diagnostic tool for lower urinary tract dysfunction.

Keywords:
bladdergene signaturemachine learningpainperformance

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Area of Science:

  • Urology
  • Genomics
  • Bioinformatics

Background:

  • Lower urinary tract dysfunction (LUTD) affects many globally, with symptom overlap hindering diagnosis of subtypes like non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder (DO).
  • Lack of reliable biomarkers complicates accurate classification and targeted treatment for LUTD, including BPS.

Purpose of the Study:

  • To identify specific mRNA signatures for non-ulcerative BPS using a machine learning (ML) approach.
  • To develop a robust classifier for BPS based on gene expression profiles.

Main Methods:

  • Utilized next-generation sequencing (NGS) transcriptome data from bladder biopsies of BPS, DO, and control patients.
  • Identified 13 candidate genes using statistical analysis, validated by Quantitative Polymerase Chain Reaction (QPCR).
  • Applied supervised and unsupervised ML algorithms to QPCR data to identify a predictive gene signature.

Main Results:

  • A panel of 13 candidate genes was identified to distinguish BPS from control and DO patients.
  • A three-mRNA signature (TPPP3, FAT1, NCALD) was confirmed as a robust classifier for non-ulcerative BPS.
  • The ML framework effectively handled limited sample sizes, demonstrating utility for signature identification.

Conclusions:

  • The identified three-mRNA signature provides a promising biomarker for non-ulcerative BPS diagnosis.
  • The ML-based approach offers a valuable methodology for gene expression signature discovery in complex diseases.
  • This study lays groundwork for improved understanding and classification of LUTD subtypes.