Serum proteome analysis detects early molecular signatures of disease progression in IgA nephropathy

Background IgA nephropathy (IgAN) is a common cause of chronic kidney disease (CKD), and identifying early molecular signatures is crucial for IgAN risk stratification and timely intervention. In this study, we used serum proteomics to investigate molecular differences between IgAN patients who progressed and those who remained stable before separation in kidney function became apparent. Methods Serum samples from 40 adults with biopsy-proven IgAN were analyzed and classified as progressors (PR; n = 13) or non-progressors (NP; n = 27) based on eGFR slope over a five–six year follow-up. Samples underwent protein depletion, SP3 digestion, TMT labeling and LC-MS/MS analysis. Differentially expressed proteins were identified using Welch’s t-test. Enrichment analysis (GO, KEGG) was performed with ShinyGO and Cytoscape. Random Forest machine learning was applied for classification modeling. Results Fifty-two proteins showed a significant differential abundance between PR and NP. Enrichment analysis revealed dysregulation in complement cascade, extracellular matrix (ECM) signaling and renin-angiotensin system in PR compared to NP. Finally, the Random Forest model on this protein set achieved an accuracy of 85.0%, and identified NCAM1, F7, ARG1, CLU and ANXA5 among the most important predictors. Conclusion Serum proteomic profiling identified early molecular differences between PR and NP IgAN patients. These findings support the utility of serum proteomics for early risk stratification in IgAN.