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This research aims to develop a clinical prediction model using tubulointerstitial molecular signatures for lupus nephritis.

April 5, 2026AutoimmunityOpen Access

Integrated bioinformatics reveals tubulointerstitial immune microenvironment signatures and machine learning-driven prognostication of clinical treatment response in lupus nephritis

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Bioinformatics identifies immune microenvironment signatures in lupus nephritis, suggesting improved treatment prediction methods.

This research aims to develop a clinical prediction model using tubulointerstitial molecular signatures for lupus nephritis.
Performed bioinformatics analysis with two gene expression datasets
Applied batch effect correction and principal component analysis (PCA)
Constructed a protein-protein interaction (PPI) network to isolate hub genes
Utilized LASSO regression to define the Nscore parameter based on selected genes
Executed immune deconvolution using the CIBERSORT algorithm
The Nscore significantly correlated with 24-h proteinuria
Successfully distinguished complete-response (CR)/partial-response (PR) from non-response (NR)
Increased presence of Tfh cells and M1 macrophages noted in NR samples
Developed a clinical nomogram that showed excellent discrimination for treatment response

Ke et al. (2026) studied this question.

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