Supervised machine learning algorithm identified<i>KRT20</i>,<i>BATF</i>and<i>TP63</i>as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients
Supervised machine learning algorithm identified<i>KRT20</i>,<i>BATF</i>and<i>TP63</i>as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients
复制标题
监督机器学习算法将<i>KRT20</i>、<i>BATF</i>和<i>TP63</i>确定为间质性膀胱炎/膀胱疼痛综合征患者膀胱活检标本的生物学相关生物标志物
DOI：
10.1111/iju.14795
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发表时间：
2022
影响因子：
2.6
通讯作者：
Yamanishi Tomonori
中科院分区：
文献类型：
--
作者：
Kamasako Tomohiko;Kaga Kanya;Inoue Ken‐ichi;Hariyama Masanori;Yamanishi Tomonori
ObjectivesThis study was carried to identify biomarkers that distinguished Hunner‐type interstitial cystitis from non-Hunner ‐type interstitial cystitis patients.MethodsTotal ribonucleic acid was purified from 212 punch biopsy samples of 89 individuals who were diagnosed as interstitial cystitis/bladder pain syndrome.为了检查患者膀胱标本的表达谱，选择了68种尿路上皮主转录因子和9种已知标记物（E-钙粘蛋白、细胞角蛋白、尿斑蛋白和音刺猬蛋白）。为了对活检样本进行分类，进行了主成分分析。采用决策树算法，以确定关键的决定因素，其中102和116膀胱标本被用于学习和validations.ResultsPrincipal成分分析分离组织从Hunner型和非Hunner型间质性膀胱炎标本的主成分轴2和4。主成分2和4分别含有尿路上皮干/祖细胞转录因子和细胞角蛋白。决策树识别KRT 20、BATF和TP 63来分类非Hunner型和Hunner型间质性膀胱炎患者。与非Hunner型间质性膀胱炎样本相比，Hunner型组织中的KRT 20较低（P< 0.001）。与Hunner型间质性膀胱炎患者的邻近粘膜相比，Hunner病变中的TP 63较低（P< 0.001）。结论KRT 20、BATF和TP 63可作为间质性膀胱炎/膀胱疼痛综合征组织分类的生物学标志物。生物学上可解释的决定因素可能有助于定义难以捉摸的间质性膀胱炎/膀胱疼痛综合征的发病机制。
ObjectivesThis study was carried out to identify biomarkers that distinguish Hunner‐type interstitial cystitis from non‐Hunner‐type interstitial cystitis patients.MethodsTotal 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.ResultsPrincipal 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 identifiedKRT20,BATFandTP63to classify non‐Hunner‐type and Hunner‐type interstitial cystitis specimens.KRT20was lower in tissues from Hunner‐type compared with non‐Hunner‐type interstitial cystitis specimens (P< 0.001).TP63was 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.ConclusionKRT20,BATFandTP63were 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.