Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients. (31st January 2022)

• Record Type: Journal Article
• Authors: Kamasako, Tomohiko; Kaga, Kanya; Inoue, Ken‐ichi; Hariyama, Masanori; Yamanishi, Tomonori
• Is Part Of: International journal of urology. Volume 29:Number 5(2022)
• Page Start: 406
• Page End: 412
• Publication Date: 2022-01-31
• Subjects: BATF -- bladder pain syndrome -- decision tree -- Hunner type -- interstitial cystitis -- KRT20 -- principal component analysis -- supervised machine learning -- TP63; Urology -- Periodicals; Genitourinary organs -- Periodicals; Urologic Diseases -- Periodicals; 616.6005
• DOI: 10.1111/iju.14795
• Languages: English
• ISSNs: 0919-8172

Abstract

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.