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Updated: Oct 5, 2025
Detection of Tissue-resident Bacteria in Bladder Biopsies by 16S rRNA Fluorescence In Situ Hybridization
Published on: October 18, 2019
Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder
Tomohiko Kamasako1, Kanya Kaga1, Ken-Ichi Inoue2
1Department of Urology, Continence Center, Dokkyo Medical University, Tochigi, Japan.
Objectives:
This study was carried out to identify biomarkers that distinguish Hunner-type interstitial cystitis from non-Hunner-type interstitial cystitis patients.
Methods:
Total ribonucleic acid was purified from 212 punch biopsy specimens of 89 individuals who were diagnosed as interstitial cystitis/bladder pain syndrome. To examine the expression profile of patients' bladder specimens, 68 urothelial master transcription factors and nine known markers (E-cadherin, cytokeratins, uroplakins and sonic hedgehog) were selected. To classify the biopsy samples, principal component analysis was carried out. A decision tree algorithm was adopted to identify critical determinants, in which 102 and 116 bladder specimens were used for learning and validation, respectively.
Results:
Principal component analysis segregated tissues from Hunner-type and non-Hunner-type interstitial cystitis specimens in principal component axes 2 and 4. Principal components 2 and 4 contained urothelial stem/progenitor transcription factors and cytokeratins, respectively. A decision tree identified KRT20, BATF and TP63 to classify non-Hunner-type and Hunner-type interstitial cystitis specimens. KRT20 was lower in tissues from Hunner-type compared with non-Hunner-type interstitial cystitis specimens (P < 0.001). TP63 was lower in Hunner's lesions compared with adjacent mucosa from Hunner-type interstitial cystitis patients (P < 0.001). Blinded validation using additional biopsy specimens verified that the decision tree showed fairly precise concordance with cystoscopic diagnosis.
Conclusion:
KRT20, BATF and TP63 were identified as biologically relevant biomarkers to classify tissues from interstitial cystitis/bladder pain syndrome specimens. The biologically explainable determinants could contribute to defining the elusive interstitial cystitis/bladder pain syndrome pathogenesis.
