ArticleiScience2024
Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- The role of classical protein tyrosine phosphatases in diabetic kidney disease and therapeutic implications.Journal of biomedical science · 2026Review
- Elevated Circulating HMGB1 Levels as a Potential Biomarker for the Diagnosis and Therapy of Heart Failure: A Cross-Sectional Study.Reviews in cardiovascular medicine · 2026Article
- Selection and Validation of Novel Biomarkers for ntOPN-Based Models for Diabetic Kidney Disease in Patients With Diabetes Mellitus.Journal of diabetes research · 2026Article
- HMGB1: a key molecule linking chronic inflammation to complications in type 2 diabetes mellitus and a target for exercise intervention.Frontiers in endocrinology · 2026Review
- Characteristics of extracellular vesicle-derived lncRNAs during the progression of HBV-related hepatocellular carcinoma.BMC cancer · 2025Article
- Discovery and serological validation of DAMP-derived B-cell epitopes as diagnostic biomarkers for diabetic nephropathy.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hinting at their biomarker potential. Combining Mfuzz clustering with weighted gene co-expression network analysis (WGCNA) highlighted five candidates (HMGB1, CD44, FBLN1, PTPRG, and ADAMTSL4). HMGB1 emerged as a promising biomarker, closely correlated with renal function changes. Experimental validation supported HMGB1's upregulation under high glucose conditions, reinforcing its potential as an early detection biomarker for DN. This research advances DN understanding and identifies five potential biomarkers, notably HMGB1, as a promising early monitoring target. These findings set the stage for future clinical diagnostic applications in DN.
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Registered trials
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