Unsupervised Clustering Reveals Distinct Subtypes of Biliary Atresia Based on Immune Cell Types and Gene Expression.

Xiuqing Pang,Yutian Chong,Jiao Gong,Guangjian Liu,Jing Cao,Zhuanggui Chen,Xinhua Li,Shuru Chen,Zhiliang Gao

doi:10.3389/fimmu.2021.720841

Abstract

BackgroundBiliary atresia (BA) is a severe cholangiopathy of early infancy that destroys cholangiocytes, obstructs ductular pathways and if left untreated, culminates to liver cirrhosis. Mechanisms underlying the etiological heterogeneity remain elusive and few studies have attempted phenotyping BA. We applied machine learning to identify distinct subtypes of BA which correlate with the underlying pathogenesis.MethodsThe BA microarray dataset GSE46995 was downloaded from the Gene Expression Omnibus (GEO) database. Unsupervised hierarchical cluster analysis was performed to identify BA subtypes. Then, functional enrichment analysis was applied and hub genes identified to explore molecular mechanisms associated with each subtype. An independent dataset GSE15235 was used for validation process.ResultsBased on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes. Functional analysis of Subtype 1 correlated with Fc Gamma Receptor (FCGR) activation and hub gene FCGR2A, suggesting an autoimmune response targeting bile ducts. Subtype 2 was associated with immune receptor activity, cytokine receptor, signaling by interleukins, viral protein interaction, suggesting BA is associated with viral infection. Subtype 3 was associated with signaling and regulation of expression of Robo receptors and hub gene ITGB2, corresponding to embryonic BA. Moreover, Reactome pathway analysis showed Neutrophil degranulation pathway enrichment in all subtypes, suggesting it may result from an early insult that leads to biliary stasis.ConclusionsThe classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.

Highlights

Biliary atresia (BA) is a severe neonatal disease that often ends with cholestasis and progressive hepatic failure
Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes
Functional analysis of Subtype 1 correlated with Fc Gamma Receptor (FCGR) activation and hub gene Fc Fragment Of IgG Receptor IIa (FCGR2A), suggesting an autoimmune response targeting bile ducts

Summary

Introduction

Biliary atresia (BA) is a severe neonatal disease that often ends with cholestasis and progressive hepatic failure. The development of the Kasai hepatic portoenterostomy (HPE) and liver transplantation have substantially improved patient outcomes. Survival rates have been associated with the time-to-surgery and early diagnosis; even after successful HPE, progression to liver injury and fibrosis is still observed in 40%-50% of patients [1]. The management of long-term complications and immune suppression is a major clinical challenge [2, 3]. Elucidating the pathogenesis underlying the extent and the source of the disease phenotype variance is key to improve current BA management and develop novel therapeutic strategies. Biliary atresia (BA) is a severe cholangiopathy of early infancy that destroys cholangiocytes, obstructs ductular pathways and if left untreated, culminates to liver cirrhosis. We applied machine learning to identify distinct subtypes of BA which correlate with the underlying pathogenesis

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