Analysis of the molecular mechanism and miRNA–mRNA regulatory network of henagliflozin in diabetic kidney disease based on machine learning

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Author: CHENG Si ¹ ZHANG Yanping ¹ LIU Xin ¹ WANG Fei ¹  ZHAO Jindong ^2, ^3, ⁴ FANG Zhaohui ^2, ^3, ⁴

Affiliation: 1. Anhui University of Traditional Chinese Medicine First Clinical Medical College, Hefei 230000, China 2. Department of Endocrinology, First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei 230031, China 3. Institute of Traditional Chinese Medicine for Diabetes Prevention and Treatment, Anhui Academy of Chinese Medical Sciences, Hefei 230038, China 4. Insititute of Health and Medicine, Hefei Comprehensive National Science Center, Center for Xin'an Medicine and Modernization of Traditional Chinese Medicine, Hefei 230038, China

Keywords: Henagliflozin Diabetic kidney disease miRNA-mRNA regulatory network Differentially expressed genes Machine learning

DOI: 10.12173/j.issn.1005-0698.202508050

Reference: CHENG Si, ZHANG Yanping, LIU Xin, WANG Fei, ZHAO Jindong, FANG Zhaohui. Analysis of the molecular mechanism and miRNA–mRNA regulatory network of henagliflozin in diabetic kidney disease based on machine learning[J]. Yaowu Liuxingbingxue Zazhi, 2026, 35(4): 409-418. DOI: 10.12173/j.issn.1005-0698.202508050.[Article in Chinese]  Copied

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Abstract

Objective To identify potential key targets of henagliflozin in the treatment of diabetic kidney disease (DKD) using transcriptomic profiling combined with ma-chine-learning algorithms, and to construct a microRNA (miRNA)-messenger RNA (mRNA) regulatory network to elucidate its underlying molecular mechanisms.

Methods The action targets of henagliflozin were obtained from public databases and subjected to pathway enrichment analysis using the DAVID platform. Transcriptomic data of DKD patients were obtained from the the GEO database. Differentially expressed genes (DEGs) were screened using R software, and three machine-learning algorithms-least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and random forest-were applied to identify consensus dis-ease-related core genes. Upstream miRNAs were predicted using the ENCORI database, core miRNAs were screened by the MCC algorithm in Cytoscape software, and the miRNA-mRNA regulatory network was constructed subsequently.

Results A total of 8,214 mRNAs expression data of DKD patients were obtained, 69 DEGs were screened out, and 8 key genes were ultimately selected using machine learning algorithms. Functional enrichment revealed substantial overlap between the core gene network and pathways associated with henagliflozin, with major enrichment in the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway.

Conclusion Henagliflozin may improve the progression of DKD by modulating the PI3K/ Akt signaling pathway and its associated miRNA-mRNA regulatory network. The identified core targets provide potential value for disease monitoring and precision intervention. This study offers a molecular basis for the precise clinical application of henagliflozin in patients with DKD.

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