Background:Systemic sclerosis (scleroderma, SSc) is a systemic autoimmune disease characterized by inflammation, fibrosis and vasculopathy and associated with high mortality and high morbidity1. Stratification based on whole-genome gene expression data could provide a new basis for clinical diagnosis from a micro perspective2.Objectives:The objective of this study is to stratify patients with SSc, combine with clinical skin scores and clinical features, and provide a preliminary assessment and novel insights for assessing disease severity, and treatment design.Methods:The original data mRNA expression profiles of GSE95065 (including 18 SSc patients and 4 healthy controls) and GSE130955 (including 58 SSc patients and 33 healthy controls) were downloaded from the public Gene Expression Omnibus (GEO) database. After batch correction, background adjustment, and other pre-processing, a large gene matrix was obtained to identify the differently expressed genes (DEGs) of SSc compared with healthy controls. Then the gene expression matrix decomposition was used to identify SSc subtypes by NMF algorithm. The cluster-based signature genes were applied to pathway enrichment analysis by Metascape3. Immune infiltrating cells and clinical skin scores were evaluated in all SSc subtypes.Results:Total 325 DEGs were imputed to NMF unsupervised machine learning algorithm. Patients were divided into 2 subtypes (Figure 1A), one of which (sub1) was mostly enriched in the defense response to bacterium and cellular response to lipopolysaccharide pathway and another subtype (sub2) was enriched in the PPAR signaling and alcohol metabolic process pathway (Figure 1B-C). According to immune infiltration, sub1 had higher level of immune cells such as B cells, CD4+T cells, DC cells, Th2 cells and Tregs compared with sub2 (P < 0.01). Sub2 had more skin-related cells, including Epithelial cells, Fibroblasts and Sebocytes (P < 0.05). Interestingly, combined with clinical information, sub1 showed a severe clinical skin score over those of Sub2 patients (P < 0.05)(Figure 1D-E).Conclusion:Our findings indicated that SSc patients could be stratified into 2 subtypes which had different molecular profiles of disease progression and clinical disease activities. This result could serve as a template for future studies to design stratified approaches for SSc patients.References:[1]Xu X, Ramanujam M, Visvanathan S, et al. Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods. PLoS One 2020;15(11):e0242863. doi: 10.1371/journal.pone.0242863 [published Online First: 2020/12/01].[2]Xu C, Meng LB, Duan YC, et al. Screening and identification of biomarkers for systemic sclerosis via microarray technology. Int J Mol Med 2019;44(5):1753-70. doi: 10.3892/ijmm.2019.4332 [published Online First: 2019/09/24].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared
BackgroundPolyarteritis nodosa (PAN) is a primary form of vasculitis characterized by inflammation of primarily medium-sized arteries[1]. Although the etiology of vasculitis is not fully understood, it has been suggested that cytokines(including interleukin-1 (IL-1) and interleukin-10(IL-10)) are significantly associated with a high susceptibility to vasculitis[2, 3]. However, there is no relevant research to explore the causality and direction of this association between interleukin and PAN.ObjectivesIn this study, we intended to detect the casual association between PAN and interleukin using two-sample Mendelian randomization (MR) analysis.MethodsThe summary statistics of PAN were obtained from the FinnGen consortium release data (82 cases and 213145 controls). We also selected genetic instruments associated with il-1 and il-10 from the study of Sun et al. (3301 sample size). Initially, SNPs associated with interleukin at the genome-wide significance threshold (p<1×10-5) were extracted. Afterwards, We used a two-sample mendelian randomization analysis to explore the causal associations between PAN with IL-1, and IL-10. The inverse variance weighted (IVW) was the primary approach to calculating the effect estimates. To increase the IVW estimates, we also used other MR methods, such as MR-Egger and weighted median. Additionally, Sensitivity analyses of evaluating the influence of outliers and pleiotropy effects were performed by a variety of MR methods. All Statistical analyses were performed using R (version 4.2.1).ResultsUsing single-nucleotide polymorphisms (SNPs) of genome-wide significance as instrumental variables, we did not find a significant association of IL-1α(odds ratio[OR] = 1.044, 95% confidence interval [CI]: 0.551 to 1.981, p = 0.895), IL-1β (odds ratio[OR] = 0.986, 95% confidence interval [CI]: 0.557 to 1.745, p = 0.962), and IL-10 (odds ratio[OR] = 1.066, 95% confidence interval [CI]: 0.621 to 1.832, p = 0.815) on PAN. Likewise, Comparable results were obtained using MR-Egger regression, and weighted median. We did not find evidence to support a causal association between IL-1α, IL-1β, IL-10, and PAN.ConclusionThe results of this study based on genetic data did not support a causal relationship between PAN and Il-1, IL-10.References[1]Saadoun D, Vautier M, Cacoub P. Medium- and Large-Vessel Vasculitis. Circulation. 2021;143(3):267-82.[2]Cavalli G, Colafrancesco S, Emmi G, Imazio M, Lopalco G, Maggio MC, et al. Interleukin 1α: a comprehensive review on the role of IL-1α in the pathogenesis and treatment of autoimmune and inflammatory diseases. Autoimmun Rev. 2021;20(3):102763.[3]Lee PY, Day-Lewis M, Henderson LA, Friedman KG, Lo J, Roberts JE, et al. Distinct clinical and immunological features of SARS-CoV-2-induced multisystem inflammatory syndrome in children. J Clin Invest. 2020;130(11):5942-50.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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