ArticleBioengineered2021
Identification of the key genes and pathways involved in B cells in primary Sjögren' s syndrome.
Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Single-cell transcriptomic analysis uncovers heterogeneity in the labial gland microenvironment of primary Sjögren's syndrome.Journal of translational autoimmunity · 2024Article
- WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes.Heliyon · 2024Article
- Application of omics in Sjögren's syndrome.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Review
- IRF9 and XAF1 as Diagnostic Markers of Primary Sjogren Syndrome.Computational and mathematical methods in medicine · 2022Article
- Novel Immune-Related Genetic Expression for Primary Sjögren's Syndrome.Frontiers in medicine · 2021Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary Sjögren' s syndrome (pSS) is a relatively common autoimmune disease, which mainly involves the exocrine glands, causing dry eye, dryness of mouth, fatigue and pain in the joints, thus severely affecting the normal lives of patients. B cell populations are considered to play an important role in their pathogenesis and pSS patients are generally characterized by exhibiting biological signs of B cell activation. Moreover, another important characterized change in the peripheral blood of pSS patients is found to be the decreased number of circulating memory B cells. However, the mechanisms underlying the B cell activation and the decreased level of circulating memory B cells in pSS patients are still unclear. Therefore, we identified key genes and pathways involved in B cells in pSS through a combination of several bioinformatic approaches including Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and weighted gene co-expression network analysis (WGCNA) using gene expression data of pSS patients and controls from an open database Gene Expression Omnibus (GEO). The results may provide some novel insights into the pathogenesis of pSS. Moreover, we constructed and validated a diagnostic model for pSS by using the expression patterns of these key genes, which may assist clinicians in diagnosing pSS.
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