Lupus Nephritis Tubulointerstitial Gene Signatures and Treatment Response Model

Two independent tubulointerstitial gene expression datasets (GSE113342 and GSE200306) underpin this research. Jialong Ke developed a clinical prediction model integrating molecular signatures and immune cell deconvolution to stratify treatment response in lupus nephritis. The study was last updated in April 2026.

Use Cases

• Predict clinical treatment response (CR/PR vs NR) using the derived Nscore parameter based on seven key genes (EGR1, IL6R, TFRC, CCL19, IFI16, IFI35, Fra1).
• Analyze correlations between the Nscore parameter and clinical metrics like 24-hour proteinuria levels.
• Deconvolute immune cell infiltration signatures, such as T follicular helper (Tfh) cells and M1 macrophages, from gene expression data using the CIBERSORT algorithm.
• Build a clinical nomogram for prognostication by integrating the Nscore with patient demographic features like sex.

Strengths

• Integrates two independent gene expression datasets (GSE113342 and GSE200306) for analysis.
• Defines a novel predictive parameter (Nscore) derived from seven specific hub genes using LASSO regression.
• Applies batch effect correction and principal component analysis (PCA) to identify differentially expressed genes.

Limitations

• The dataset is small, consisting of a 657.9 KB DOCX file, indicating limited raw data scope.
• Specific row counts, column names, and sample-level data are unavailable for direct analysis.
• Geographic and demographic coverage of the underlying patient samples is not specified.

Provenance

Source

figshare, authored by Jialong Ke.

Collection Method

Bioinformatics analysis of public tubulointerstitial gene expression datasets with batch correction, PCA, DEG identification, PPI network analysis, and LASSO regression.

Freshness

Last updated in April 2026.