Identification of immune-associated genes and single-cell sequencing analysis in diagnosing Behçet's disease.

Behçet's Disease (BD) is diagnosed as continuing systematic inflammation, lacking clarified etiology and pertinent detection of clinical usage. We screened bulk-seq and single-cell seq data of BD from GEO database, then investigated immune infiltration landscape in BD and further explored main immune cells in a single-cell resolution. It was inferred that monocytes might be the trigger cell of BD occurrence and development. On the other hand, we performed differentially expression analysis and weighted correlation network analysis, and intersected the selected genes with immune-related genes from ImmPort database. After KEGG/GO-enrichment, Protein-Protein Interaction network and miRNA-mRNA regulatory network were constructed. Meanwhile, we utilized the Least Absolute Shrinkage and Selection Operator to filtrate 5 significant intersected genes for BD's diagnosis (NRTN, TRDJ1, IGLV4-69, PDIA2 and AVPR1A), then drew Operating Characteristic curve to analyze diagnosis value. For experimental validation, the BD mice models were constructed and qRT-PCR experiment was performed to validate the expression level of selected genes. In conclusion, we investigated the immune landscape in BD and explored the role of monocyte in BD occurrence and development. Through gene selection and machine learning, five new potential diagnostic biomarkers were discovered and validated in both external dataset and experiment assay levels.