ArticleFrontiers in immunology2023
Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Single-Cell RNA Sequencing Revealed the Role of Interferon-Gamma Related Genes in Primary Sjögren's Syndrome.Journal of cellular and molecular medicine · 2026Article
- Interferon signaling gene expression, and DNA methylation interactions in Sjögren's disease via mendelian randomization.iScience · 2026Article
- Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.BMC pharmacology & toxicology · 2026Article
- Integrative Bioinformatics and Machine Learning Identify Novel Diagnostic Biomarkers and Molecular Mechanisms in Sjögren's Syndrome.International journal of genomics · 2026Article
- Identification of childhood-onset lupus nephritis through integrative bioinformatics: otoferlin as a novel biomarker.Pediatric research · 2025Article
- Identifying nexilin as a central gene in neutrophil-driven abdominal aortic aneurysm pathogenesis.Molecular medicine (Cambridge, Mass.) · 2025Article
- Spatiotemporal imaging of immune dynamics: rethinking drug efficacy evaluation in cancer immunotherapy.Frontiers in immunology · 2025Article
- DNA methylation and whole-genome transcription analysis in CD4Clinical epigenetics · 2024Article
- Article
- Identification and Validation of IFI44 as a Novel Biomarker for Primary Sjögren's Syndrome.Journal of inflammation research · 2024Article
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11 authors.
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Abstract
Background: Sjögren's syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis. Methods: We downloaded three datasets of SS patients' and healthy pepole's whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers' diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied. Results: We obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2. Conclusion: In this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients.
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