Identification of ferroptosis-related diagnostic markers in primary Sjögren's syndrome based on machine learning.

Abstract

Primary Sjogren's syndrome (pSS) is a common autoimmune disorder that affects up to 0.3-3% of the global population. Ferroptosis has recently been identified to play a significant role in autoimmune diseases. However, the molecular mechanisms of ferroptosis in the initiation and progression of pSS remains unclear. To investigate the molecular mechanisms underlying the occurrence and progression of pSS, we utilized a comprehensive approach by integrating data obtained from the Gene Expression Omnibus (GEO) database with data from the FerrDb database to identify the ferroptosis-related differentially expressed genes (DEGs). Furthermore, we implemented an innovative transcriptomic analysis method utilizing a computer-aided algorithm to establish a network between hub genes associated with ferroptosis and the immune microenvironment in pSS patients. Our results revealed significant differences in the gene expression profiles of pSS samples compared to normal tissues, with 1,830 significantly up-regulated genes and 1,310 significantly down-regulated genes. In addition, our results showed a significant increase in the proportions of B cells and CD4+ T cells in pSS samples compared to normal tissues. AND then, our analysis revealed that a combination of six ferroptosis-related genes, including TBK1, SLC1A4, PIK3CA, ENO3, EGR1, and ATG5, could serve as optimal markers for the diagnosis of pSS. The combined analysis of these six genes accurately diagnosed the occurrence of pSS. This study offers valuable insights into the pathogenesis of pSS and highlights the importance of targeting ferroptosis-related DEGs, which suggests a novel treatment strategy for pSS.