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Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning
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Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning
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Journal Article
Identification of diagnostic markers for diabetic kidney disease by weighted gene co-expression network analysis and machine learning
2026
Overview
Diabetic kidney disease (DKD) represents a major complication associated with diabetes mellitus, notably contributing to patient morbidity and mortality. However, early diagnosis of DKD remains challenging due to the lack of clear diagnostic biomarkers. Therefore, in the present study, microarray and RNA-sequencing data from the Gene Expression Omnibus database were systematically analyzed. Using differential expression and weighted gene co-expression network analysis, 49 genes with marked expression changes in DKD were identified. Subsequent analyses, including functional enrichment, protein-protein interaction network construction, machine learning techniques and assessment of immune cell infiltration, led to the identification of three hub genes: Spleen-associated tyrosine kinase, apoptotic peptidase activating factor 1 and ADAM metallopeptidase domain 10, as promising diagnostic markers, which were further evaluated by receiver operating characteristic curve analysis. Expression changes of the identified hub genes were validated in both DKD mouse models and clinical patient samples. Collectively, the present study provided a novel perspective on the molecular basis of DKD, and highlighted novel candidates for potential diagnostic and therapeutic applications.
