Background: 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.

Background: The global prevalence of Crohn disease (CD), a chronic inflammatory disease of the intestine, has been increasing; however, the etiology and pathogenesis of this disease have not been fully elucidated. Therefore, in the present study, we aimed to better understand the molecular mechanisms underlying CD to aid the development of novel therapeutic strategies for this condition. Methods: Based on the transcriptome data from patients with CD, this study used an unsupervised learning method to construct gene co-expression molecular subgroups and the R and SPSS software to identify the biological, clinical, and genetic characteristics and signatures of each subgroup. Results: Two subgroups were analyzed. Compared to subgroup II, subgroup I consisted of older patients with a more limited range of disease presentation and had a higher number of smokers. The specific genes associated with this subgroup, including CDKN2B, solute carrier family 22 member 5, and phytanoyl-CoA 2-hydroxylase, can be targeted for managing intestinal dysbacteriosis. The number of patients showing infiltrating lesions was higher, the number of smokers was lower, and CD severity was worse in patients in subgroup II than those in subgroup I. The specific genes relevant to subgroup II included cluster of differentiation 44, tryptophanyl-tRNA synthetase, and interleukin 10 receptor, alpha subunit, which may be related to viral infection. Conclusion: The present study segregated patients with CD into 2 subgroups; the findings reported herein provide a new theoretical basis for the diagnosis and treatment of CD and could aid a thorough identification of potential therapeutic targets for this disease.