Identification of Key Genes for Early Diagnosis of Diabetic Kidney Disease Using WGCNA Analysis: Insights into Disease Mechanisms at the Transcriptome Level
Identification of Key Genes for Early Diagnosis of Diabetic Kidney Disease Using WGCNA Analysis: Insights into Disease Mechanisms at the Transcriptome Level
Introduction: Diabetic kidney disease (DKD) is one of the most serious complications of diabetes, primarily caused by microvascular damage in the kidneys. Early diagnosis of DKD is critical for preventing its progression, yet reliable biomarkers remain limited. This study aims to identify key genes involved in the early stages of DKD through transcriptome-level analysis, providing insights into potential biomarkers for early diagnosis and understanding the underlying mechanisms of disease development.
Methods: We performed a weighted gene co-expression network analysis (WGCNA) on renal glomerular tissue samples from the publicly available GSE30528 dataset. Through WGCNA, key gene modules related to DKD were identified. Gene ontology (GO) and REACTOME pathway enrichment analyses were employed to explore the biological roles of these genes.
Results: Four gene modules were identified as significant through WGCNA, and subsequent enrichment analysis revealed their involvement in pathways such as immune system regulation, collagen biosynthesis, striated muscle contraction, and neutrophil degranulation. From these modules, four hub genes—CHI3L1, RARRES1, TNNT2, and PCOLCE2—were identified as potential early biomarkers for DKD.
Conclusion: The identification of CHI3L1, RARRES1, TNNT2, and PCOLCE2 provides novel insights into the molecular mechanisms driving DKD. These genes represent promising targets for early diagnosis and offer potential avenues for therapeutic intervention in DKD.