Integrative Bioinformatics and Machine Learning Identify Novel Diagnostic Biomarkers and Molecular Mechanisms in Sjögren’s Syndrome
Hua Xu, Yong Liu, Yuyin Song, Yifan Zheng, Haifeng Jing, Yanfei Gao, Depeng Zhou, Xiang Chi, Jia Chen
TL;DR
This study uses bioinformatics and machine learning to find new biomarkers and understand the molecular basis of Sjögren’s Syndrome, a difficult-to-diagnose autoimmune disease.
Contribution
The study introduces 12 novel diagnostic biomarkers for Sjögren’s Syndrome and reveals their molecular mechanisms and immune-related functions.
Findings
Twelve hub genes (e.g., EPSTI1, IFIH1) were identified with strong diagnostic performance across multiple datasets.
Immune infiltration analysis showed immune dysregulation in SS patients, including reduced CD8+ T cells and Tregs.
Drug repurposing suggested FDA-approved drugs like nisoldipine and exemestane as potential treatments.
Abstract
Sjögren’s syndrome (SS) is a chronic autoimmune disorder characterized by significant diagnostic challenges due to nonspecific symptoms and a lack of reliable biomarkers, often resulting in delayed diagnosis and suboptimal patient management. This study is aimed at identifying novel diagnostic biomarkers and elucidating the molecular mechanisms underlying SS pathogenesis through integrative bioinformatics and machine learning approaches. We analyzed three peripheral blood transcriptomic datasets (GSE51092, GSE66795, and GSE84844) comprising a total of 351 SS patients and 91 healthy controls. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and 12 machine learning algorithms were employed to identify robust diagnostic biomarkers. Immune cell infiltration was assessed using CIBERSORT, and single‐cell RNA sequencing data (GSE157278) were analyzed to…
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Taxonomy
TopicsSalivary Gland Disorders and Functions · GDF15 and Related Biomarkers · Single-cell and spatial transcriptomics