Objective To analyze the cuproptosis⁃associated genes related to diabetic nephropathy (DN) by bioinformatics method, and to predict targeted Traditional Chinese Medicines on these genes. Methods Data sets (GSE96804 and GSE30529) of genes expressions related to DN were downloaded from the database of GEO, and cuproptosis⁃associated genes were obtained from relevant literature. Based on GSE96804 data set, differentially expressed cuproptosis⁃associated genes (DECAGs) of DN were screened, and the correlation of expressions between DECAGs was analyzed. The cluster typing analysis and gene set variation analysis (GSVA) were performed on DN samples according to DECAGs expressions. Weighted gene co⁃expression network analysis was performed based on the results of cluster typing for acquiring disease⁃critical gene module and core genes. Differentially expressed genes of GSE96804 data set were obtained by employing the limma program package, and then the intersection genes were obtained by intersecting with core genes. Disease⁃defining genes were screened through 3 categories of machine learning models based on GSE96804 data set and intersection genes. Diagnostic value of defining genes on DN was evaluated based on GSE96804 data set through the receiver operating characteristic curve, and the validation was performed based on GSE30529 data set. Key DECAGs were obtained through analyzing the correlation of disease⁃defining genes with DECAGs. In database of CoreMineTM MEDICAL, Traditional Chinese Medicine prediction for DECAGs was conducted. Results A total of 7 DECAGs were obtained, therein DBT, PDHA1, and FDX1 expressions positively correlated with GCSH expression, DLAT and PDHA1 expressions positively correlated with FDX1 expression, and GLS expression negatively correlated with DLAT expression. DN samples could be assigned to two clusters (C1 cluster and C2 cluster) based on DECAGs. GSVA results revealed that compared with C1 cluster, NOD⁃like receptor (NLR), Notch, Toll⁃like receptor (TLR), and other signaling pathways were enriched in C2 cluster. Brown module (containing 369 core genes) was highly correlated with cluster typing based on DECAGs. A total of 114 intersection genes were obtained, and disease⁃defining gene G6PC was finally screened out through 3 categories of machine learning models. In the data sets of GSE96804 and GSE30529, areas under the curve of G6PC expression for diagnosing DN were 0.987 and 0.900, respectively. PDHA1, FDX1, DBT, and GCSH were correlated with G6PC, which therefore could be regarded as key DECAGs. According to DECAGs, a total of 168 flavors of Traditional Chinese Medicines were screened out, the four properties of Traditional Chinese Medicine were mainly cold, warm and moderate, the five flavors were mainly bitter and sweet, meridians mainly belonged to liver meridian, spleen meridian, and kidney meridian, and the effects were mainly categories of restoring qi, clearing heat and promoting blood circulation to remove stagnation, therein Radix salviae, Caulis sinomenii, Lithospermum erythrorhizon, Fu Shen, Atractylodes macrocephala, Sophora alopecuroides, Cnidii fructus, and Folium artemisiae argyi all targeted at least 2 DECAGs. Conclusion Seven DECAGs and their related NLR, Notch, TLR and other signaling pathways may be important links to cuproptosis⁃associated pathogenesis of DN, among which PDHA1, FDX1, DBT and GCSH are key DECAGs, and G6PC is the disease⁃defining gene of DN. Radix salviae, Caulis sinomenii, Lithospermum erythrorhizon, and other Traditional Chinese Medicines can treat DN through regulating cuproptosis.

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