Development and validation of a heme metabolism-related genes signature for diagnosis and immunological characterization of lupus nephritis.

BACKGROUND

Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus (SLE). Heme metabolism, a critical component of energy metabolism and redox homeostasis, has been strongly implicated in the pathogenesis and progression of various tissue diseases. This study aims to develop accurate and effective diagnostic biomarkers for LN based on heme metabolism-related genes (HMGs) to enhance early diagnosis and precision treatment of LN.

METHODS

This study is based on transcriptomic data and clinical information from LN patients (human kidney tissue samples) in the GEO database, diagnostic genes for LN were identified through differential analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression analysis. Further, single-gene gene set enrichment analysis (GSEA) investigated the potential biological functions and signaling pathways associated with diagnostic genes in LN. Immunocellular infiltration levels in LN and Control groups were assessed utilizing CIBERSORT and ssGSEA. To elucidate the regulatory mechanisms of diagnostic genes, corresponding transcription factor and microRNA regulatory networks were constructed. Additionally, based upon the expression profiles of diagnostic genes, LN patients were molecularly subtyped utilizing consensus clustering analysis.

RESULTS

This study identified four diagnostic genes for LN (BTG2, CD163, UCP2, and LMO2) and constructed a diagnostic model with robust predictive performance for LN. ssGSEA immune infiltration analysis indicated that most immune related functions and immune cell infiltration levels were significantly elevated in the LN group. KEGG enrichment analysis further revealed that diagnostic genes were enriched in the Toll-like receptor signaling pathway. Through consensus clustering analysis, LN samples were divided into two molecular subtypes with significant differences.

CONCLUSION

The diagnostic model constructed drawing on HMGs can effectively distinguish LN patients and their immune characteristics, thereby providing a new perspective on the relationship between HMGs and LN.