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

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