Integrative Bioinformatics and Machine Learning Identify Novel Diagnostic Biomarkers and Molecular Mechanisms in Sjogren's Syndrome.

ABSTRACT:

Background

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.

Objective

This study is aimed at identifying novel diagnostic biomarkers and elucidating the molecular mechanisms underlying SS pathogenesis through integrative bioinformatics and machine learning approaches.

Methods

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 ro

SUBMITTER: Xu H 

PROVIDER: S-EPMC12811409 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature