Bioinformatics analysis of efferocytosis-related genes in diabetic kidney disease and screening of targeted traditional Chinese medicine.
10.19540/j.cnki.cjcmm.20250402.501
- Author:
Yi KANG
1
;
Qian JIN
2
;
Xue-Zhe WANG
1
;
Meng-Qi ZHOU
3
;
Hui-Juan ZHENG
4
;
Dan-Wen LI
1
;
Jie LYU
4
;
Yao-Xian WANG
4
Author Information
1. Dongzhimen Hospital, Beijing University of Chinese Medicine Beijing 100700, China Beijing University of Chinese Medicine Beijing 100029, China.
2. Beijing University of Chinese Medicine Beijing 100029, China.
3. Puren Hospital,Beijing 100062, China.
4. Dongzhimen Hospital, Beijing University of Chinese Medicine Beijing 100700, China.
- Publication Type:Journal Article
- Keywords:
bioinformatics;
diabetic kidney disease;
efferocytosis;
machine learning;
molecular docking
- MeSH:
Humans;
Computational Biology;
Diabetic Nephropathies/physiopathology*;
Protein Interaction Maps;
Medicine, Chinese Traditional;
Drugs, Chinese Herbal;
Phagocytosis/genetics*;
Efferocytosis
- From:
China Journal of Chinese Materia Medica
2025;50(14):4037-4052
- CountryChina
- Language:Chinese
-
Abstract:
This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
