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

Immunoglobulin A 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.

Serum samples from 40 adults with biopsy-proven IgAN were analysed and classified as progressors (PRs; n = 13) or non-progressors (NPs; n = 27) based on the estimated glomerular filtration rate slope over a 5- to 6-year follow-up. Samples underwent protein depletion, SP3 digestion, tandem mass tag labelling and liquid chromatography-tandem mass spectrometry analysis. Differentially expressed proteins were identified using Welch's t-test. Enrichment analysis (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes) was performed with ShinyGO and Cytoscape. Random forest machine learning was applied for classification modelling.

Fifty-two proteins showed a significant differential abundance between PRs and NPs. Enrichment analysis revealed dysregulation in complement cascade, extracellular matrix signalling and renin-angiotensin system in PRs compared with NPs. 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.

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.