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Identification of Key Genes Related to Glutamine Metabolism in Diabetic Nephropathy by Machine Learning Methods
112 Pages Posted: 2 Jul 2024
More...Abstract
Background: Diabetic nephropathy (DN) is an important microvascular complication of diabetes. A growing body of evidence has indicated that the dysregulation of glutamine metabolism relates to the development of DN. Therefore, the objective of this study was to elucidate the changes of glutamine metabolism-related genes (GMRGs) in DN.
Results: To begin, we identified 9 differentially expressed GMRGs (DE-GMRGs) by crossing 103 GMRGs and 2,281 DEGs from the GSE142153 dataset across the normal and DN groups. Interestingly, DE-GMRGs detected in autosomes were discovered to be considerably concentrated in glutamine metabolism processes as well as alanine, aspartate, and glutamate metabolism pathways. Furthermore, the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) approaches were utilized to identify the key genes SLC7A5 and SLC25A12. Where SLC7A5 was highly expressed in DN, and the opposite was true for SLC25A12. Besides, the key genes' remarkable diagnostic potential was further demonstrated by calculating the area under the receiver operating characteristic (ROC) curve. Finally, correlation analysis revealed a substantial association between the key genes and critical clinical indicators such as glomerular filtration rate (GFR) and serum creatinine, as well as immune cells (such as mast cells and effector memory CD8 T cells). The drug prediction and molecular docking data suggested that Bisphenol A could be an effective therapeutic agent targeting key genes.
Conclusion: In summary, the glutamine metabolism-related SLC7A5 and SLC25A12 genes were identified as prospective diagnostic and therapeutic candidates for DN in this investigation. These findings are clinically significant and may aid in the diagnosis and management of DN.
Funding: This research received no external funding.
Declaration of Interest: The authors declare that they have no competing interests.
Ethical Approval: All participants provided informed consent for their involvement. The study protocol received ethical approval from the ethics committee of The First Affiliated Hospital of Zhengzhou University.
Keywords: Diabetic nephropathy, glutamine metabolism, machine learning, drug
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