Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease
Objective: To predict the autophagy-related pathogenesis and key diagnostic genes of diabetic kidney disease (DKD) through bioinformatics analysis, and to identify related Chinese medicines. Methods: Data from sequencing microarrays GSE30528, GSE30529, and GSE1009 in the Gene Expression Omnibus (GEO...
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Digital Chinese Medicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589377725000266 |
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| author | Yufeng Xing Zining Peng Chaoyang Ye |
| author_facet | Yufeng Xing Zining Peng Chaoyang Ye |
| author_sort | Yufeng Xing |
| collection | DOAJ |
| description | Objective: To predict the autophagy-related pathogenesis and key diagnostic genes of diabetic kidney disease (DKD) through bioinformatics analysis, and to identify related Chinese medicines. Methods: Data from sequencing microarrays GSE30528, GSE30529, and GSE1009 in the Gene Expression Omnibus (GEO) were employed. Differentially expressed genes (DEGs) with adjusted P < 0.05 from GSE30528 and GSE30529 were identified. Combining these DEGs with the human autophagy gene database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and protein-protein interaction (PPI) network analysis were conducted on the obtained DKD autophagy-related genes. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were adopted to select autophagy-related genes. The diagnostic capability of these genes was assessed through analysis with the external validation set from microarray GSE1009, and relevant Chinese medicines were inversely predicted using the SymMap database. Results: A total of <styled-content style-type=''number''>2014</styled-content> DEGs were selected from GSE30528 and GSE30529, leading to the identification of 37 DKD autophagy-related genes. GO analysis indicated 681 biological mechanisms, including autophagy regulation and plasma membrane microdomain activity. KEGG enrichment analysis identified 112 related signaling pathways. PPI network analysis showed a marked enrichment of autophagy-related genes in DKD. Through LASSO regression and SVM-RFE, four core diagnostic genes for autophagy in DKD were identified: protein phosphatase 1 regulatory subunit 15A (PPP1R15A), hypoxia inducible factor 1 alpha subunit (HIF1α), deleted in liver cancer 1 (DLC1), and ceroid lipofuscinosis neuronal 3 (CLN3). The external validation set demonstrated high diagnostic efficiency for these genes. Finally, 146 kinds of potential Chinese medicines were predicted using the SymMap database, with heat-clearing and detoxifying medicine and blood-activating and stasis-eliminating medicine accounting for the largest proportion (25/146 and 13/146, respectively). Conclusion: This study analyzed and validated bioinformatics sequencing databases to elucidate the potential molecular mechanisms of DKD autophagy and predicted key diagnostic genes, potential therapeutic targets, and related Chinese medicines, laying a solid foundation for clinical research and application. |
| format | Article |
| id | doaj-art-7004cc7b564f4206bf4deafb346cb218 |
| institution | OA Journals |
| issn | 2589-3777 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Chinese Medicine |
| spelling | doaj-art-7004cc7b564f4206bf4deafb346cb2182025-08-20T01:49:58ZengKeAi Communications Co., Ltd.Digital Chinese Medicine2589-37772025-03-0181909910.1016/j.dcmed.2025.03.008Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney diseaseYufeng Xing0Zining Peng1Chaoyang Ye2Institute of Kidney Disease, Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China; Department of Nephrology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China; Key Laboratory of Liver and Kidney Diseases, Ministry of Education/Shanghai Key Laboratory of Traditional Chinese Clinical Medicine, Shanghai 200120, ChinaFirst Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, Yunnan 650500, ChinaInstitute of Kidney Disease, Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China; Department of Nephrology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China; Key Laboratory of Liver and Kidney Diseases, Ministry of Education/Shanghai Key Laboratory of Traditional Chinese Clinical Medicine, Shanghai 200120, China; Corresponding author:Objective: To predict the autophagy-related pathogenesis and key diagnostic genes of diabetic kidney disease (DKD) through bioinformatics analysis, and to identify related Chinese medicines. Methods: Data from sequencing microarrays GSE30528, GSE30529, and GSE1009 in the Gene Expression Omnibus (GEO) were employed. Differentially expressed genes (DEGs) with adjusted P < 0.05 from GSE30528 and GSE30529 were identified. Combining these DEGs with the human autophagy gene database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and protein-protein interaction (PPI) network analysis were conducted on the obtained DKD autophagy-related genes. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were adopted to select autophagy-related genes. The diagnostic capability of these genes was assessed through analysis with the external validation set from microarray GSE1009, and relevant Chinese medicines were inversely predicted using the SymMap database. Results: A total of <styled-content style-type=''number''>2014</styled-content> DEGs were selected from GSE30528 and GSE30529, leading to the identification of 37 DKD autophagy-related genes. GO analysis indicated 681 biological mechanisms, including autophagy regulation and plasma membrane microdomain activity. KEGG enrichment analysis identified 112 related signaling pathways. PPI network analysis showed a marked enrichment of autophagy-related genes in DKD. Through LASSO regression and SVM-RFE, four core diagnostic genes for autophagy in DKD were identified: protein phosphatase 1 regulatory subunit 15A (PPP1R15A), hypoxia inducible factor 1 alpha subunit (HIF1α), deleted in liver cancer 1 (DLC1), and ceroid lipofuscinosis neuronal 3 (CLN3). The external validation set demonstrated high diagnostic efficiency for these genes. Finally, 146 kinds of potential Chinese medicines were predicted using the SymMap database, with heat-clearing and detoxifying medicine and blood-activating and stasis-eliminating medicine accounting for the largest proportion (25/146 and 13/146, respectively). Conclusion: This study analyzed and validated bioinformatics sequencing databases to elucidate the potential molecular mechanisms of DKD autophagy and predicted key diagnostic genes, potential therapeutic targets, and related Chinese medicines, laying a solid foundation for clinical research and application.http://www.sciencedirect.com/science/article/pii/S2589377725000266BioinformaticsDifferentially expressed genesDiabetic kidney diseaseAutophagy genesPrediction of Chinese medicines |
| spellingShingle | Yufeng Xing Zining Peng Chaoyang Ye Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease Digital Chinese Medicine Bioinformatics Differentially expressed genes Diabetic kidney disease Autophagy genes Prediction of Chinese medicines |
| title | Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease |
| title_full | Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease |
| title_fullStr | Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease |
| title_full_unstemmed | Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease |
| title_short | Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease |
| title_sort | bioinformatics based analysis of autophagy related genes and prediction of potential chinese medicines in diabetic kidney disease |
| topic | Bioinformatics Differentially expressed genes Diabetic kidney disease Autophagy genes Prediction of Chinese medicines |
| url | http://www.sciencedirect.com/science/article/pii/S2589377725000266 |
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