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October 18, 2025Renal FailureOpen Access

Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation

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Authors

SCShengnan ChenLCLei ChenRDRuiqing Dong

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Overview

Multi-omics analysis identifies biomarkers for diabetic kidney disease, suggesting pathways for targeted treatment.

Key Points

  • Identifying 11 genes causally linked to diabetic kidney disease revealed new potential biomarkers, supporting better intervention approaches.
  • Machine learning methods demonstrated high predictive accuracy for biomarkers like LRG1 and PEX6 in diagnosing diabetic kidney disease.
  • Functional enrichment analyses pointed to inflammatory and immune responses as key dysregulated processes in both humans and animal models.
  • This research utilizes multi-omics approaches to enhance our understanding of diabetic kidney disease and develop therapeutic strategies.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68f408995de60f8893c6fdechttps://doi.org/10.1080/0886022x.2025.2568649
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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. 2Identifying Key Genes for Diabetic Kidney Disease by Bioinformatics Analysis2023
  3. 3Identifying key genes for diabetic kidney disease by bioinformatics analysis2023 · 6 citations
  4. 4Multi-omics Analyses Identify AKR1A1 as a Biomarker for Diabetic Kidney Disease2024 · 19 citations
  5. 5Identification of diagnostic markers for diabetic kidney disease by weighted gene co‑expression network analysis and machine learning2026