Identification of Anoikis-Related Genes and Immune Infiltration Characteristics in Sjögren's Syndrome Based on Machine Learning

Lei Wang¹Zi-Qi Xu¹Xinpeng Zhou¹Ying Liu^1*Mengjie Wang^1,2*

• ¹Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China
• ²Shandong University of Traditional Chinese Medicine, Jinan, China

Objective: Anoikis 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 and their molecular mechanisms in SS using public databases. Methods: SS datasets were retrieved from the GEO database. In total, 924 ARGs were extracted from the GeneCards and Harmonizome databases, followed by DEGs analysis and WGCNA. Machine learning algorithms were utilized to screen candidate biomarkers, and their diagnostic effectiveness was assessed using ROC curve analysis. Concurrently, a mouse model of SS was established and validated through in vivo experiments. Immune cell infiltration in SS tissues was evaluated using CIBERSORT, and correlations between characteristic genes and immune cell profiles were analyzed. t Potential drug candidates targeting these genes were identified using the DGIdb database. Subsequently, an lncRNA-miRNA-mRNA network associated 2 with these genes was constructed, and preliminary experimental validation was conducted. Results: A total of 35 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. Given the unclear roles of SKI and PRDX4 in SS, the study focused specifically on five relevant genes, MAPK3, IL15, S100A9, IFI27, and CXCL10, which were validated by in vivo experiments. Immune cell analysis revealed increased proportions of B cells, T cells, macrophages, and other immune cells in SS tissues. Furthermore, ceRNA and drug-gene interaction networks were established, underscoring the regulatory significance of five key miRNAs in SS. In addition, eight candidate drugs were identified with potential for modulating SS pathogenesis. Conclusion: This study substantiates the significant involvement of anoikis in SS and suggests that MAPK3, IL15, S100A9, IFI27, and CXCL10 may serve as critical biomarkers in the inflammatory progression of SS. These genes likely mediate their effects by influencing immune cell infiltration, participating in immune regulation, and modulating inflammatory responses. Our findings offer new insights into drug selection and immunotherapeutic strategies for SS.