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July 21, 2025PLoS ONEOpen Access

Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning

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Authors

HLHailin LiQLQuhuan LiZFZuyan Fan

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Overview

Single-cell transcriptomic analysis reveals ARHGEF26 downregulation in podocytes during early diabetic kidney disease, highlighting its potential as a diagnostic biomarker and therapeutic target.

Key Points

  • Identify novel podocyte molecular markers and investigate their cellular heterogeneity and pathogenic mechanisms during early diabetic kidney disease.
  • Analyzed single-cell RNA sequencing data from patients with early diabetic kidney disease using subcluster clustering, functional enrichment, and ligand-receptor interaction profiling.
  • Applied multiple machine-learning algorithms to construct diagnostic models and screen for hub differentially expressed podocyte marker genes.
  • Validated the expression and diagnostic value of candidate biomarkers using external transcriptomic datasets, RT-qPCR, and Western blot assays.
  • Demonstrated significant podocyte heterogeneity and altered ligand-receptor communications in early diabetic kidney disease progression.
  • Identified and confirmed ARHGEF26 as a hub podocyte gene that is significantly downregulated in diabetic kidney disease, establishing its viability as a diagnostic biomarker.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/6a741bcdf36dc1ab06603870https://doi.org/10.1371/journal.pone.0328352
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Also Consider

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  1. 11785-LB: Potential Noninvasive Markers Predicting Diabetic Kidney Disease Development2024
  2. 2Transcriptomic Profiling Combined with Machine Learning and Mendelian Randomization Identifies Diagnostic Biomarkers and Immune Infiltration Patterns in Diabetic Kidney Disease2026
  3. 3Urinary podocyte markers in diabetic kidney disease2024 · 2 citations
  4. 4Identification of diagnostic markers for diabetic kidney disease by weighted gene co‑expression network analysis and machine learning2026
  5. 5Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation2025 · 1 citations