Identification of anoikis-related genes and immune infiltration characteristics in Sjögren's syndrome based on machine learning.

Objective: Anoikis, a recently identified type of programmed cell death analogous to apoptosis, has been implicated in the pathogenesis of Sjögren's syndrome (SS). Although accumulating evidence indicates its involvement in modulating immune responses and contributing to SS progression, the precise role of anoikis in SS remains inadequately understood. This study aimed to explore anoikis-related genes (ARGs) and their molecular mechanisms in SS using public databases. Methods: SS datasets (GSE23117, GSE84844 and GSE12795) were retrieved from the GEO database. In total, 924 ARGs were extracted from the GeneCards and Harmonizome databases, followed by differential expression gene (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were utilized to screen candidate biomarkers, and their diagnostic effectiveness was assessed using receiver operating characteristic (ROC) curve analysis. Concurrently, a mouse model of SS was established and validated through Results: A total of 35 differentially expressed anoikis-related genes (DEARGs) were identified. GO and KEGG enrichment analyses demonstrated that DEARGs were primarily associated with inflammation, viral infections, and the necroptosis signaling pathway. Machine learning analysis pinpointed 14 feature genes, among seven were associated with cancer ( Conclusion: This study substantiates the significant involvement of anoikis in SS and suggests that