Identification and validation of immune-associated NETosis subtypes and biomarkers in anti-neutrophil cytoplasmic antibody associated glomerulonephritis

BackgroundNETosis is a new form of cell death, marked by DNA chromatin release from dead neutrophils. While it aids in microbe defense, it may worsen inflammation in autoimmune diseases, causing tissue harm. The impact of NETosis on Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN) remains unexplored and requires investigation. MethodsFirst, a weighted gene co-expression network analysis (WGCNA) was conducted to uncover differential expression of neutrophil extranuclear trap-associated genes (DE-NETs) in ANCA-GN. The NETosisScore model was established through the single sample gene set enrichment analysis (ssGSEA), which categorized all patients into high-risk and low-risk groups. The accuracy of model was assessed by ROC curve. The biological function of various subgroups was explored through Gene Set Variation Analysis (GSVA), while the abundance of immune cell infiltration was measured with CIBERSORT. Furthermore, the key NETosis-related genes (NRGs) were identified using three machine learning algorithms, and their relationship with renal function was analyzed through the NephroseqV5 database. Through the application of qPCR and immunohistochemical staining techniques, the mRNA and protein expression levels of NRGs were determined in patients with ANCA-GN and control. ResultsA NETosisScore model was developed from 18 DE-NETs using the ssGSEA algorithm. The model's ability to predict ANCA-GN patients with a ROC AUC of 0.921. The high-risk group in ANCA-GN showed enrichment of immune-related pathways and greater infiltration of immune cells, as revealed by KEGG enrichment analysis and CIBERSORT. Using three machine learning algorithms, we identified six NRGs. Significant positive correlations were found between NRGs and CCR, macrophages, T-cell co-inhibition, and TIL. Further KEGG analysis revealed that the functions of NRGs may be closely related to the toll-like receptor signaling pathway. The levels of NRGs increased as kidney function declined and were positively correlated with Scr (serum creatinine) and negatively correlated with GFR (glomerular filtration rate), qPCR analysis showed increased expression of most NRGs in ANCA-GN patients. Furthermore, immunohistochemical staining confirmed higher expression of all NRGs in ANCA-GN patients. ConclusionNETosisScore model accurately predicts high-risk patients in ANCA-GN with enriched immune pathways, 6 NRGs identified as potential biomarkers.

Automated summary

This study investigated the association between NETosis and ANCA-associated Glomerulonephritis (ANCA-GN). Differential gene expression analysis and machine learning algorithms were employed to identify NETosis-related genes (NRGs) and develop a NETosisScore model for predicting high-risk patients. Enrichment analysis, immune cell infiltration analysis, and correlation analysis revealed the involvement of immune pathways and the potential biomarker roles of NRGs in ANCA-GN.