Advances in Mass Spectrometry-Based Proteomics for Kidney Disease Biomarker Discovery and Clinical Translation.

Abstract

The high prevalence and substantial burden of kidney diseases necessitate advanced approaches to elucidate molecular mechanisms and promote precision medicine. Mass spectrometry (MS)-based proteomics has evolved into a widely used analytical platform, delivering high-sensitivity, high-throughput protein profiling capabilities that have contributed substantially to biomarker discovery, mechanistic dissection, and therapeutic target identification across a broad range of kidney diseases. This review provides a comprehensive overview of MS-based proteomics applications in kidney disease research, covering progress in biomarker identification, pathogenic mechanism interrogation, and clinical translation. It highlights methodological advances, emerging trends, and persistent challenges that shape the field. Existing literature has uncovered abundant disease-specific biomarkers and revealed key pathogenic pathways, including podocyte injury-associated protein interaction networks, dysregulated complement activation, and metabolic reprogramming, and have critically assessed their translational potential. Additionally, investigations into posttranslational modifications such as phosphorylation and glycosylation have provided valuable insights for targeted therapies. Collectively, these findings illustrate the contributions of MS-based proteomics to the characterization of disease molecular heterogeneity, the identification of key pathogenic drivers, and the development of precision medicine approaches. This review further addresses current challenges in clinical applications, including sample heterogeneity, data complexity, and standardization issues. We additionally emphasize the critical need for minimum reporting standards and multicenter harmonization to accelerate the clinical translation of renal proteomics. Future research should focus on integrating multiomics and artificial intelligence-driven data mining to enhance precise disease subtyping, dynamic monitoring, and personalized treatment strategies.