• Jianglei Ma, Huijie Zhang, Weijiang Chu, Pengyu Wang, Huaqiu Chen, Yuanyuan Zhang, and Guangming Wang.
• School of Clinical Medicine, Dali University, Dali, China.
• Medicine (Baltimore). 2022 Dec 23; 101 (51): e32274e32274.

BackgroundSystemic 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.MethodsThe 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.ResultsPatients 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.ConclusionWe 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.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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