Potential common mechanisms between primary Sjögren's syndrome and Hashimoto's thyroiditis: A public databases-based study

Yanjun Lin¹Shupin Tang²Yan Lin¹Rihui Wang³Yifeng Xing¹Zonghe Xu¹Yan Li¹Qingxia Fang¹Wenwei Wei¹Dong Wu^4*Jiang Chen^5,6*

• ¹Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
• ²Department of Otorhinolaryngology-Head and Neck Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
• ³Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
• ⁴Research Center of Dental and Craniofacial Implants, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
• ⁵Affiliated Stomatological Hospital of Fujian Medical University, Fuzhou, China
• ⁶Department of Oral Implantology, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China

Objective Primary Sjögren's syndrome (pSS) and Hashimoto thyroiditis (HT) can occur in the same patient population, but the mechanism of co-occurrence remains unknown. This study aims to explore the underlying mechanism. Methods We screened differentially expressed genes (DEGs) in the pSS and HT-related transcriptomic microarrays. Based on KEGG, PID, Reactome, and BioCarta enrichment analysis, pathway annotations were performed. A PPI network was developed using STRING. Betweenness, BottleNeck, MNC, Radiality EPC, and Stress topological analyses were performed to identify hub genes. Then, we used two more datasets to validate the key genes. Immune infiltration landscape of pSS and HT were profiled based on CIBERSORT, Xcell, MCPCounter, and EPIC. Correlation between T/B cells and key genes was performed. Single gene GSEA analysis was performed to further explore enriched pathways of key genes. Finally, we predicted the drugs of key genes and the cross-talk genes targeted in the protein domain. Results A total of 93 cross-talk genes were found. These genes were mainly related to the immune system. STAT1, CD8A, and PTPRC were identified as hub genes using six topological methods. STAT1 and PTPRC are considered key genes after in silico validation. STAT1 and PTPRC were linked to CD8+ Tcm and other immune cells in the pSS and HT dataset. GSEA analysis showed that STAT1 and PTPRC may play a role in pSS and HT through several pathways, including IFNγ response, IFNα response, allograft rejection, E2F targets, complement, G2M checkpoint, IL6-JAK-STAT3 signaling, KRAS signaling up, IL2-STAT5 signaling, IL6-JAK-STAT3-signaling, and inflammatory response. Guttiferone K and picoplatin may be the candidate drugs for the treatment of pSS and HT. Cross-talk genes were mainly enriched in IGc1, MHCIIα and SCY. Conclusions We analysed databases and gene expression data for pSS and HT. We identified two genes (STAT1, PTPRC) as potential biomarkers of disease activity in pSS and HT. We also gained new insights into the cellular and molecular mechanisms associated with pSS and HT. Based on the key genes and cross-talk genes, we predicted potential drugs and protein domains for pSS and HT.