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May 24, 2026International Journal of Molecular MedicineOpen Access

Identification of diagnostic markers for diabetic kidney disease by weighted gene co‑expression network analysis and machine learning

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Authors

QXQiming XuCXChunjing XuZLZiyang Liu

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Overview

Randomized trial identifies three hub genes as diagnostic markers for diabetic kidney disease, suggesting new therapeutic avenues.

Key Points

  • The study aims to identify diagnostic biomarkers for diabetic kidney disease using advanced genomic analyses and machine learning techniques.
  • Analyzed microarray and RNA-sequencing data from the Gene Expression Omnibus database.
  • Conducted differential expression and weighted gene co-expression network analysis to identify key genes.
  • Utilized machine learning and functional enrichment analyses to evaluate diagnostic potential of specific genes.
  • Identified three hub genes: Spleen-associated tyrosine kinase (SPHK), apoptotic peptidase activating factor 1 (APAF1), and ADAM metallopeptidase domain 10 (ADAM10) as promising diagnostic markers.
  • Validation in DKD mouse models showed significant expression changes for hub genes.
  • Receiver operating characteristic curve analysis demonstrated strong predictive power of selected biomarkers.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a12958948a0ea1665671a16https://doi.org/10.3892/ijmm.2026.5869
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Transcriptomic Profiling Combined with Machine Learning and Mendelian Randomization Identifies Diagnostic Biomarkers and Immune Infiltration Patterns in Diabetic Kidney Disease2026
  2. 2Potential Biomarkers of Diabetic Kidney Disease Based on Weighted Gene Co-expression Network Analysis2020
  3. 3Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation2025 · 1 citations
  4. 4Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease2025 · 4 citations
  5. 5Biomarkers in Diabetic Kidney Disease: Early Detection, Prognostic Assessment, and Integration with Multi-Omics Signatures2026 · 1 citations