SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis

Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential therapeutic target for DCI through integrated machine learning and molecular docking analyses. Transcriptomic data from cortical neuronal samples of T2DM patients were analysed using support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, revealing SERPINA3 as a significantly upregulated gene in DCI. Experimental validation via Western blot confirmed elevated SERPINA3 protein levels in DCI patient plasma. Molecular docking demonstrated the stable binding of sulfonylurea hypoglycaemic agents, such as gliclazide and glimepiride, to SERPINA3, with binding energies of -6.8 and -6.6 kcal/mol, respectively. These findings suggest that SERPINA3 plays a pivotal role in DCI pathogenesis and that sulfonylurea drugs may exert neuroprotective effects through SERPINA3-mediated pathways. This study provides novel insights into the molecular mechanisms of DCI and highlights the potential of SERPINA3-targeted therapies for early intervention and treatment. Further research is warranted to validate these findings in larger cohorts and explore their clinical applicability.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2025-02-24
DOI
https://doi.org/10.3390/ijms26051947
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis

Guangyi Xu, Zhaoming Cao, Yu An, Yanhui Lu et al.
International Journal of Molecular Sciences
Computational Drug Discovery Methods
article

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis

Guangyi Xu, Zhaoming Cao, Yu An, Yanhui Lu, Jie Zheng, Yage Du, Ziyuan Wang, Yinchao Ma, Jingya Wang
article en

Abstract

Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential therapeutic target for DCI through integrated machine learning and molecular docking analyses. Transcriptomic data from cortical neuronal samples of T2DM patients were analysed using support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, revealing SERPINA3 as a significantly upregulated gene in DCI. Experimental validation via Western blot confirmed elevated SERPINA3 protein levels in DCI patient plasma. Molecular docking demonstrated the stable binding of sulfonylurea hypoglycaemic agents, such as gliclazide and glimepiride, to SERPINA3, with binding energies of -6.8 and -6.6 kcal/mol, respectively. These findings suggest that SERPINA3 plays a pivotal role in DCI pathogenesis and that sulfonylurea drugs may exert neuroprotective effects through SERPINA3-mediated pathways. This study provides novel insights into the molecular mechanisms of DCI and highlights the potential of SERPINA3-targeted therapies for early intervention and treatment. Further research is warranted to validate these findings in larger cohorts and explore their clinical applicability.

International Journal of Molecular SciencesVol. 26(5)
Capital Medical University (CN), Peking University (CN), Beijing Chao-Yang Hospital, Capital Medical University (CN), Beijing Academy of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 96%
Computational Drug Discovery Methods
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