Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36713418", "kind": "science-record", "revision": 3, "content_hash": "sha256:74d662a3f5210c7e13efdcd52d5e820e100c810d0d4e11e319f0206dd7e77827", "created_at": "2026-11-01T19:01:16Z", "updated_at": "2026-11-01T22:08:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis.", "doi": "10.3389/fimmu.2022.1008246", "pmid": "36713418", "authors": ["Dingding Wang", "Shen Yang", "Yong Zhao", "Yanan Zhang", "Kaiyun Hua", "Yichao Gu", "Shuangshuang Li", "Junmin Liao", "Ting Yang", "Jiawei Zhao", "Jinshi Huang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Biliary atresia (BA) is the most common form of severe neonatal obstructive jaundice. The etiology and pathogenesis of BA are multifactorial, and different factors may interact to produce heterogeneous pathological features and clinical outcomes. Despite different pathological features, all patients received the same treatment strategy. This study performed integrative clustering analysis based on multiple high-throughput datasets to identify the molecular subtypes of BA and provide a new treatment strategy for personalized treatment of the different subtypes of BA.\nMethods: The RNA sequence dataset GSE122340 in the Gene Expression Omnibus (GEO) database was downloaded; 31 BA and 20 control normal liver tissues were collected at our center for transcriptome sequencing, and clinical and follow-up data of BA patients were available. Molecular subtypes were identified using integrated unsupervised cluster analysis involving gene expression, biliary fibrosis, and immune enrichment scores based on the transcriptome dataset, and the results were validated using independent datasets.\nResults: Based on the results of the integrated unsupervised clustering analysis, four molecular subtypes were identified: autoimmune, inflammatory, virus infection-related, and oxidative stress. The autoimmune subtype with a moderate prognosis was dominated by autoimmune responses and morphogenesis, such as the Fc-gamma receptor and Wnt signaling pathway. The biological process of the inflammatory subtype was mainly the inflammatory response, with the best prognosis, youngest age at surgery, and lowest liver stiffness. The virus infection-related subtype had the worst prognosis and was enriched for a variety of biological processes such as viral infection, immunity, anatomical morphogenesis, and epithelial mesenchymal transition. The oxidative stress subtype was characterized by the activation of oxidative stress and various metabolic pathways and had a poor prognosis. The above results were verified independently in the validation sets.\nConclusions: This study identified four molecular subtypes of BA with distinct prognosis and biological processes. According to the pathological characteristics of the different subtypes, individualized perioperative and preoperative treatment may be a new strategy to improve the prognosis of BA.", "claims": [{"id": "claim-method", "statement": "Similarity Network Fusion and Consensus clustering (SNF-CC) algorithm in “CancerSubtypes” R package (DOI:10.18129/B9.bioc.CancerSubtypes, version 1.20.0) was performed to identify molecular classification of patients with BA in the training and validation datasets (15).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36713418", "locator": "sections[8] \"Identification of molecular subtypes\" paragraph 2", "quote": "Similarity Network Fusion and Consensus clustering (SNF-CC) algorithm in “CancerSubtypes” R package (DOI:10.18129/B9.bioc.CancerSubtypes, version 1.20.0) was performed to identify molecular classification of patients with BA in the training and validation datasets (15)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36713418", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified four molecular subtypes of biliary atresia—autoimmune, inflammatory, virus infection-related, and oxidative stress—using integrated unsupervised clustering of gene expression, biliary fibrosis, and immune enrichment data, with the classification independently validated.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36713418", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Based on the results of the integrated unsupervised clustering analysis, four molecular subtypes were identified: autoimmune, inflammatory, virus infection-related, and oxidative stress."}, {"asset": "source:pmid:36713418", "locator": "sections[16] \"Subtype identification of BA\" paragraph 2", "quote": "The silhouette width was 0.85, suggesting that the clustering results were reliable with high degree of cohesion and separation"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified four molecular subtypes of biliary atresia—autoimmune, inflammatory, virus infection-related, and oxidative stress—using integrated unsupervised clustering of gene expression, biliary fibrosis, and immune enrichment data, with the classification independently validated.", "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:36713418"], "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. 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