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Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder. In patients with childhood SLE (cSLE), the onset of the disease occurs before 18 years of age and accounts for a high proportion of childhood autoimmune diseases. Adult SLE and cSLE differ in terms of clinical manifestations, gene...

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Autores principales: Ma, Jianglei, Zhang, Huijie, Chu, Weijiang, Wang, Pengyu, Chen, Huaqiu, Zhang, Yuanyuan, Wang, Guangming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794347/
https://www.ncbi.nlm.nih.gov/pubmed/36595784
http://dx.doi.org/10.1097/MD.0000000000032274
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author Ma, Jianglei
Zhang, Huijie
Chu, Weijiang
Wang, Pengyu
Chen, Huaqiu
Zhang, Yuanyuan
Wang, Guangming
author_facet Ma, Jianglei
Zhang, Huijie
Chu, Weijiang
Wang, Pengyu
Chen, Huaqiu
Zhang, Yuanyuan
Wang, Guangming
author_sort Ma, Jianglei
collection PubMed
description Systemic lupus erythematosus (SLE) is a complex autoimmune disorder. In patients with childhood SLE (cSLE), the onset of the disease occurs before 18 years of age and accounts for a high proportion of childhood autoimmune diseases. Adult SLE and cSLE differ in terms of clinical manifestations, gene expression profiles, and treatment. Because current diagnostic methods do not meet clinical requirements, researchers currently use transcriptome analysis to investigate the characteristics of the cSLE genome. In the present study, we used bioinformatics methods to genotype cSLE and identify potential therapeutic targets. METHODS: The transcriptomes of 952 patients with cSLE and 94 normal controls were obtained from the Gene Expression Omnibus using unsupervised class learning to determine the genotypes in the microarray dataset, and the clinical characteristics, differentially expressed genes, and biological characteristics of the subtypes were analyzed. RESULTS: Patients with cSLE were accordingly classified into three subgroups. Subgroup I was associated with lupus nephritis, female patients, and a high SLE disease activity index, and the disease in this subgroup was more severe than that in other subgroups. The SLE disease activity index in subgroup II was low; this subgroup may be related to lupus vasculitis. Subgroup III mostly included male patients and was associated with neuropsychiatric manifestations of lupus. CONCLUSION: We divided patients with cSLE into three subgroups with different characteristics based on transcriptome data. Our findings provide molecular evidence for future diagnosis and individualized treatment of cSLE.
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spelling pubmed-97943472022-12-29 Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics Ma, Jianglei Zhang, Huijie Chu, Weijiang Wang, Pengyu Chen, Huaqiu Zhang, Yuanyuan Wang, Guangming Medicine (Baltimore) 3600 Systemic lupus erythematosus (SLE) is a complex autoimmune disorder. In patients with childhood SLE (cSLE), the onset of the disease occurs before 18 years of age and accounts for a high proportion of childhood autoimmune diseases. Adult SLE and cSLE differ in terms of clinical manifestations, gene expression profiles, and treatment. Because current diagnostic methods do not meet clinical requirements, researchers currently use transcriptome analysis to investigate the characteristics of the cSLE genome. In the present study, we used bioinformatics methods to genotype cSLE and identify potential therapeutic targets. METHODS: The transcriptomes of 952 patients with cSLE and 94 normal controls were obtained from the Gene Expression Omnibus using unsupervised class learning to determine the genotypes in the microarray dataset, and the clinical characteristics, differentially expressed genes, and biological characteristics of the subtypes were analyzed. RESULTS: Patients with cSLE were accordingly classified into three subgroups. Subgroup I was associated with lupus nephritis, female patients, and a high SLE disease activity index, and the disease in this subgroup was more severe than that in other subgroups. The SLE disease activity index in subgroup II was low; this subgroup may be related to lupus vasculitis. Subgroup III mostly included male patients and was associated with neuropsychiatric manifestations of lupus. CONCLUSION: We divided patients with cSLE into three subgroups with different characteristics based on transcriptome data. Our findings provide molecular evidence for future diagnosis and individualized treatment of cSLE. Lippincott Williams & Wilkins 2022-12-23 /pmc/articles/PMC9794347/ /pubmed/36595784 http://dx.doi.org/10.1097/MD.0000000000032274 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 3600
Ma, Jianglei
Zhang, Huijie
Chu, Weijiang
Wang, Pengyu
Chen, Huaqiu
Zhang, Yuanyuan
Wang, Guangming
Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title_full Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title_fullStr Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title_full_unstemmed Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title_short Construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
title_sort construction of molecular subgroups in childhood systemic lupus erythematosus using bioinformatics
topic 3600
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794347/
https://www.ncbi.nlm.nih.gov/pubmed/36595784
http://dx.doi.org/10.1097/MD.0000000000032274
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