ArticleRheumatology international2015
Identification of potential genomic biomarkers for Sjögren's syndrome using data pooling of gene expression microarrays.
Article in Rheumatology international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 54 citations in OpenAlex.
- Mitochondrial RNA-Type I Interferon Axis in Sjögren's Disease: Molecular Mechanisms and Translational Implications.International journal of molecular sciences · 2026Review
- A Distinguishable Peripheral Blood and Conjunctival Transcriptome and Gut Microbiome in Sjögren's Disease: A Pilot Study.Eye & contact lens · 2025Article
- Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren's syndrome based on comprehensive bioinformatics and immunohistochemical analyses.Clinical and experimental medicine · 2024Article
- WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes.Heliyon · 2024Article
- Identification of EPSTI1 as a new potential biomarker for SLE based on GEO database.Clinical rheumatology · 2024Article
- UnravelingFrontiers in oncology · 2024Review
- Salivary diagnostics: Bridging dentistry and medicine: A systematic review.Bioinformation · 2024Article
- Integrated Analysis Identifies Upregulated SAMD9L as a Potential Biomarker Correlating with the Severity of Primary Sjögren's Syndrome.Journal of inflammation research · 2023Article
- Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis.Frontiers in medicine · 2023Article
- Article
- Potential common molecular mechanisms between Sjögren syndrome and inclusion body myositis: a bioinformatic analysis andFrontiers in immunology · 2023Article
- Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution.bioRxiv : the preprint server for biology · 2022Article
- Transcriptomes of peripheral blood mononuclear cells from juvenile dermatomyositis patients show elevated inflammation even when clinically inactive.Scientific reports · 2022Article
- Differential long non-coding RNA expression profile and function analysis in primary Sjogren's syndrome.BMC immunology · 2021Article
- Tetraspanin CD53 Promotes Lymphocyte Recirculation by Stabilizing L-Selectin Surface Expression.iScience · 2020Article
- IRT5 Probiotics Changes Immune Modulatory Protein Expression in the Extraorbital Lacrimal Glands of an Autoimmune Dry Eye Mouse Model.Investigative ophthalmology & visual science · 2020Article
- A New Approach for the Diagnosis of Systemic and Oral Diseases Based on Salivary Biomolecules.Disease markers · 2019Review
- Plasmacytoid DCs From Patients With Sjögren's Syndrome Are Transcriptionally Primed for Enhanced Pro-inflammatory Cytokine Production.Frontiers in immunology · 2019Article
- ElevatedFrontiers in immunology · 2019Article
- Type I Interferon Signaling Is Required for Dacryoadenitis in the Nonobese Diabetic Mouse Model of Sjögren Syndrome.International journal of molecular sciences · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sjögren's syndrome (SS) is an autoimmune disease characterized by lymphocytic infiltration and destruction of salivary and lacrimal glands. The diagnosis of SS can be challenging due to lack of a specific test for the disease. The purpose of this study is to examine the accuracy of using gene expression profile for diagnosis of SS. We identified 9 publically available datasets that included gene expression data from saliva and salivary gland biopsy samples of 52 patients with SS and 51 controls. Out of these datasets, we compiled and pooled data from three datasets that included 37 and 29 samples from SS patients and healthy controls, respectively, which were designated as "training set." Then, we performed cross-listing in a group of independent gene expression datasets from patients with SS to identify consensus gene list of differentially expressed genes. We performed Linear Discriminant Analysis (LDA) to quantify the accuracy of discriminating genes to predict SS in both the "training set" and an independent group of datasets that was designated as "test set." We identified 55 genes as potential classifier genes to differentiate SS from healthy controls. An LDA by leave-one-out cross-validation method identified 19 genes (EPSTI1, IFI44, IFI44L, IFIT1, IFIT2, IFIT3, MX1, OAS1, SAMD9L, PSMB9, STAT1, HERC5, EV12B, CD53, SELL, HLA-DQA1, PTPRC, B2M, and TAP2) with highest classification accuracy rate (95.7 %). Moreover, we validated our results by reproducing the same gene expression profile as a discriminatory test in the "test set," which included data from salivary gland samples of 15 patients with SS and 22 controls with 94.6 % accuracy. We propose that gene expression profile in the saliva or salivary glands could represent a promising simple and reproducible diagnostic biomarker for SS.
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