# The novel diagnostic markers for systemic lupus erythematosus and periodontal disease

## TL;DR

This study identifies LY96 and TMEM140 as potential diagnostic and therapeutic markers for systemic lupus erythematosus and periodontal disease by analyzing shared genetic patterns.

## Contribution

The study discovers novel shared diagnostic markers (LY96 and TMEM140) for SLE and PD through integrated multi-omics analysis.

## Key findings

- LY96 and TMEM140 were identified as hub genes associated with both SLE and PD.
- The hub genes showed significant diagnostic performance across multiple datasets and experimental validations.
- Shared hub genes were linked to immune and metabolic processes in peripheral blood.

## Abstract

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.

Microarray datasets for systemic lupus erythematosus (SLE) and periodontal disease (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 module genes overlapping between the significant correlation modules 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 module 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.

Using WGCNA, we identified significant correlation modules and overlapping module 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.

LY96 andTMEM140 can be used as new diagnostic and therapeutic markers for SLE and PD.