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BackgroundSystemic lupus erythematosus (SLE) is a multisystem autoimmune disease that commonly affects the kidneys. Research into markers that can predict the prognosis of tubulointerstitial lupus nephritis (LN) has been impeded by the lack of well-designed studies.MethodsIn this study, we selected and merged 3 sets of renal biopsy tubulointerstitial data from GSE32591, GSE69438, and GSE127797, including 95 LN and 15 living healthy donors. CIBERSORTx was utilized for differentially infiltrating immune cell (DIIC) analysis. Weighted Gene Co-Expression network analysis (WGCNA) was employed to explore differentially expressed gene (DEG) related modules. Combined WGCNA hub genes and protein-protein interaction (PPI) validation was used for immune marker identification. Lastly, unsupervised clustering was carried out to validate the correlation between these markers and clinical characteristics.ResultsOur findings unveiled TYROBP, C1QB, LAPTM5, CTSS, PTPRC as the 5 immune markers, which were negatively correlated with glomerular filtration rate (GFR). Specifically, the expression levels of TYROBP and C1QB were significantly different between proliferative LN (PLN) and membranous LN (MLN). Unsupervised clustering could aggregate LN by these immune marker expression spectrums.ConclusionsThis study is the first to identify infiltrating immune cells and associated molecular patterns in the tubulointerstitium of LN by utilizing bioinformatics methods. These findings contribute to a better understanding of the mechanisms behind LN, and promote more precise diagnosis.

annals of translational medicine

Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis