Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients

Int J Urol. 2022 May;29(5):406-412. doi: 10.1111/iju.14795. Epub 2022 Jan 31.

Abstract

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.

Keywords: BATF; KRT20; TP63; Hunner type; bladder pain syndrome; decision tree; interstitial cystitis; principal component analysis; supervised machine learning.

MeSH terms

  • Basic-Leucine Zipper Transcription Factors / metabolism
  • Biomarkers / metabolism
  • Biopsy
  • Cystitis, Interstitial* / pathology
  • Female
  • Hedgehog Proteins / metabolism
  • Humans
  • Keratin-20
  • Male
  • Supervised Machine Learning
  • Transcription Factors / metabolism
  • Tumor Suppressor Proteins / metabolism
  • Urinary Bladder / pathology

Substances

  • BATF protein, human
  • Basic-Leucine Zipper Transcription Factors
  • Biomarkers
  • Hedgehog Proteins
  • KRT20 protein, human
  • Keratin-20
  • TP63 protein, human
  • Transcription Factors
  • Tumor Suppressor Proteins