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

Background: Biliary 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.

Methods: The 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.

Results: 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 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.

Conclusions: The classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.