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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34646264", "kind": "science-record", "revision": 3, "content_hash": "sha256:87eee1e8b7cd516faa6d5ffe63d2440e1fb04f13f7c280ec5f9e5a56bb79a265", "created_at": "2026-11-01T17:36:17Z", "updated_at": "2026-11-01T21:26:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unsupervised Clustering Reveals Distinct Subtypes of Biliary Atresia Based on Immune Cell Types and Gene Expression.", "doi": "10.3389/fimmu.2021.720841", "pmid": "34646264", "authors": ["Xiuqing Pang", "Jing Cao", "Shuru Chen", "Zhiliang Gao", "Guangjian Liu", "Yutian Chong", "Zhuanggui Chen", "Jiao Gong", "Xinhua Li"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "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.\nMethods: 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.\nResults: 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.\nConclusions: The classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.", "claims": [{"id": "claim-method", "statement": "The ensemble Similarity Network Fusion and Consensus clustering algorithm (SNF-CC) was used to observe gene expression patterns across BA patients and cluster identification.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34646264", "locator": "sections[4] \"Cluster Analysis\" paragraph 2", "quote": "The ensemble Similarity Network Fusion and Consensus clustering algorithm (SNF-CC) was used to observe gene expression patterns across BA patients and cluster identification."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34646264", "locator": "sections[13] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34646264", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes."}, {"asset": "source:pmid:34646264", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The BA microarray dataset GSE46995 was downloaded from the Gene Expression Omnibus (GEO) database."}, {"asset": "source:pmid:34646264", "locator": "sections[8] \"Subtype Identification of Biliary Atresia\" paragraph 2", "quote": "By applying an ensemble method of SNF-CC, BA datasets were clustered into three distinct subtypes ( Figures 2D, E ) with a silhouette width value of 0.91, suggesting a high degree of cohesion and separation among each subtype in the silhouette plot ( Figure 2D )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34646264"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15235"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15235"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34646264", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2021.720841", "sha256": "sha256:9e41e553e2873e711f5fecad26b7c2256e037633362eb9baf03628df8bbdad0a", "bytes": 35683, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2021.720841", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8502897", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15235", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15235", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15235", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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