AUTHOR=Guo Qingbao , Fan Yan-Na , Xie Manli , Wang Qian-Nan , Li Jingjie , Liu Simeng , Wang Xiaopeng , Yu Dan , Zou Zhengxing , Gao Gan , Zhang Qian , Hao Fangbin , Feng Jie , Yang Rimiao , Wang Minjie , Fu Heguan , Bao Xiangyang , Duan Lian TITLE=Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships JOURNAL=Frontiers in Immunology VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1456392 DOI=10.3389/fimmu.2024.1456392 ISSN=1664-3224 ABSTRACT=Background

Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD).

Methods

We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes.

Results

By intersecting the important module genes from WGCNA with the DEGs, the study highlighted CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P as key crosstalk genes linking SLE and MMD. GO analysis indicated that these shared genes were predominantly enriched in immune system process and immune response. LASSO analysis identified MPZL3 as the optimal shared diagnostic biomarkers for both SLE and MMD. Additionally, the analysis of immune cell infiltration revealed the significant involvement of activation of T and monocytes cells in the pathogenesis of SLE and MMD.

Conclusion

This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P have been identified as key crosstalk genes that connect MMD and SLE. Activation of T and monocytes cells-mediated immune responses are proposed to play a significant role in the association between MMD and SLE.