Table_3_Unsupervised Clustering Reveals Distinct Subtypes of Biliary Atresia Based on Immune Cell Types and Gene Expression.xlsx

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.

背景背景性胆道闭锁（BA）是一种早期婴儿期严重的胆管疾病，它破坏胆管细胞，阻塞导管途径，如未得到治疗，最终导致肝硬化。其病因异质性的潜在机制尚不明确，且很少有研究尝试对BA进行表型分析。本研究应用机器学习技术，旨在识别与潜在发病机制相关的BA不同亚型。方法从基因表达综合数据库（GEO）下载了BA微阵列数据集GSE46995。通过无监督分层聚类分析，确定了BA亚型。随后，应用功能富集分析并鉴定了关键基因，以探索与每个亚型相关的分子机制。独立数据集GSE15235用于验证过程。结果基于无监督聚类分析，BA患者可以被分为三种不同的亚型：自身免疫型、病毒型和胚胎型。亚型1的功能分析关联到Fc伽马受体（FCGR）的激活和关键基因FCGR2A，提示针对胆管的自身免疫反应。亚型2与免疫受体活性、细胞因子受体、白介素信号传导、病毒蛋白相互作用相关，表明BA与病毒感染相关。亚型3与Robo受体的信号传导和表达调控相关，关键基因ITGB2对应于胚胎型BA。此外，Reactome通路分析显示所有亚型中均富集了中性粒细胞脱颗粒通路，这可能源于导致胆汁淤滞的早期损伤。结论将BA分类为不同的亚型，提高了我们对BA潜在发病机制的理解，并为未来的研究提供了新的见解。