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Updated: Apr 24, 2026
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Predictive urinary biomarkers for steroid-resistant and steroid-sensitive focal segmental glomerulosclerosis using
Shiva Kalantari, Mohsen Nafar, Dorothea Rutishauser
1Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden. Roman.Zubarev@ki.se.
Background:
Focal segmental glomerulosclerosis (FSGS) is a glomerular scarring disease diagnosed mostly by kidney biopsy. Since there is currently no diagnostic test that can accurately predict steroid responsiveness in FSGS, prediction of the responsiveness of patients to steroid therapy with noninvasive means has become a critical issue. In the present study urinary proteomics was used as a noninvasive tool to discover potential predictive biomarkers.
Methods:
Urinary proteome of 10 patients (n = 6 steroid-sensitive, n = 4 steroid-resistant) with biopsy proven FSGS was analyzed using nano-LC-MS/MS and supervised multivariate statistical analysis was performed.
Results:
Twenty one proteins were identified as discriminating species among which apolipoprotein A-1 and Matrix-remodeling protein 8 had the most drastic fold changes being over- and underrepresented, respectively, in steroid sensitive compared to steroid resistant urine samples. Gene ontology enrichment analysis revealed acute inflammatory response as the dominant biological process.
Conclusion:
The obtained results suggest a panel of predictive biomarkers for FSGS. Proteins involved in the inflammatory response are shown to be implicated in the responsiveness. As a tool for biomarker discovery, urinary proteomics is especially fruitful in the area of prediction of responsiveness to drugs. Further validation of these biomarkers is however needed.
