The novel diagnostic markers for systemic lupus erythematosus and periodontal disease

Cheng Xuedi^1*Zhang Jinfeng²Hou Jiali³Han Xiaocui¹Han Bin¹Zhou Jun¹Wang Zhongjun¹Wang Junzheng³

• ¹The Affiliated Hospital of Qingdao University, Qingdao, China
• ²Qingdao Women and Children's Hospital, Qingdao, Shandong Province, China
• ³Qingdao Haici medical group, Qingdao, China

Background and aims: Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, characterized by aberrant activation of the immune system that leads to diverse clinical symptoms; periodontal disease (PD) is an inflammatory oral disorder caused by immune-mediated damage against subgingival microflora. Although clinical evidence suggests a potential association between SLE and PD, their shared pathogenic mechanisms remain unclear. This study aims to explore common genetic markers in SLE and PD that hold diagnostic and therapeutic implications.Methods: Microarray datasets for SLE and PD were obtained from the Gene Expression Omnibus (GEO) database. Module genes between the two diseases were screened using Weighted Gene Co-expression Network Analysis (WGCNA), and model genes overlapping between the significant correlation models of GSE61635 and GSE16134 were identified. Functional enrichment analyses of genes within overlapping modules and their significantly correlated associated modules were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Overlapping model genes underwent differential expression analysis in GSE16134. A diagnostic model was constructed using the Random Forest (RF) machine learning technique under Receiver Operating Characteristic (ROC) curve assessment, which top 10 key genes were screened and analyzed for differential expression across three datasets (GSE61635, GSE10334, and GSE50772) to identify hub genes. Protein-protein interaction (PPI) network analysis was conducted to explore relationships between hub genes. CIBERSORT and Gene Set Variation Analysis (GSVA) were used to evaluate the correlation between shared hub genes and immune infiltration patterns as well as metabolic pathways. Finally, hub genes were validated using additional datasets, single-cell RNA sequencing (scRNA-seq) data, and immunohistochemistry (IHC) experiments.Results: Using WGCNA, we identified significant correlation models and overlapping model genes, which were subjected to differential expression analysis in different datasets. Further, 4 hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The additional datasets, scRNA-seq and IHC results verified that LY96 and TMEM140, possessing the promising diagnostic and therapeutic performance.Conclusion: LY96 andTMEM140 can be used as new diagnostic and therapeutic markers for SLE and PD.