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Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis
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,...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878701/ https://www.ncbi.nlm.nih.gov/pubmed/36713418 http://dx.doi.org/10.3389/fimmu.2022.1008246 |
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author | Wang, Dingding Yang, Shen Zhao, Yong Zhang, Yanan Hua, Kaiyun Gu, Yichao Li, Shuangshuang Liao, Junmin Yang, Ting Zhao, Jiawei Huang, Jinshi |
author_facet | Wang, Dingding Yang, Shen Zhao, Yong Zhang, Yanan Hua, Kaiyun Gu, Yichao Li, Shuangshuang Liao, Junmin Yang, Ting Zhao, Jiawei Huang, Jinshi |
author_sort | Wang, Dingding |
collection | PubMed |
description | 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-9878701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98787012023-01-27 Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis Wang, Dingding Yang, Shen Zhao, Yong Zhang, Yanan Hua, Kaiyun Gu, Yichao Li, Shuangshuang Liao, Junmin Yang, Ting Zhao, Jiawei Huang, Jinshi Front Immunol Immunology 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9878701/ /pubmed/36713418 http://dx.doi.org/10.3389/fimmu.2022.1008246 Text en Copyright © 2023 Wang, Yang, Zhao, Zhang, Hua, Gu, Li, Liao, Yang, Zhao and Huang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Wang, Dingding Yang, Shen Zhao, Yong Zhang, Yanan Hua, Kaiyun Gu, Yichao Li, Shuangshuang Liao, Junmin Yang, Ting Zhao, Jiawei Huang, Jinshi Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title | Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title_full | Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title_fullStr | Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title_full_unstemmed | Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title_short | Identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
title_sort | identifying and validating molecular subtypes of biliary atresia using multiple high-throughput data integration analysis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878701/ https://www.ncbi.nlm.nih.gov/pubmed/36713418 http://dx.doi.org/10.3389/fimmu.2022.1008246 |
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