Evidence map›Paper›PMID 40671224›Full record

ArticleCurrent Alzheimer research2025

Effective Analysis of Alzheimer's Disease and Mechanisms of Methyl-4- Hydroxybenzoate using Network Toxicology, Molecular Docking, and Machine Learning Strategies.

Jianren Wen, Jingxuan Hu, Xue Yang, Feifei Luo, Guohui Zou

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Article in Current Alzheimer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jianren WenJiangxi University of Traditional Chinese Medicine, Nanchang, China.
Jingxuan HuJiangxi University of Traditional Chinese Medicine, Nanchang, China.
Xue YangJiangxi University of Traditional Chinese Medicine, Nanchang, China.
Feifei LuoJiangxi University of Traditional Chinese Medicine, Nanchang, China.
Guohui ZouJiangxi University of Traditional Chinese Medicine, Nanchang, China.

Funding

Innovation and Entrepreneurship Training Program for College Students S202410412112National Natural Science Foundation of China 81760811, 81960854, 82260911Natural Science Foundation of Jiangxi Province 20192B AB205099, 20202BA206070Science and Technology Department of Jiangxi Province 20243BCE51028, 310201384304Science and Technology Program of Jiangxi Provincial Health Commission 20204420, SKJP-220210188, 2022B516State Administration of Traditional Chinese Medicine has built a high-level key discipline of traditional Chinese medicine zyyzdxk-2023113
6 · The paper itself

Abstract

introductionNowadays, the large increase in environmental pollutants has led to the occurrence and development of an increasing number of diseases. Studies have shown that exposure to environmental pollutants, such as methyl-4-hydroxybenzoate (MEP) may lead to Alzheimer's disease (AD). Therefore, the purpose of this study was to elucidate the complex effects and potential molecular mechanisms of environmental pollutants MEP on AD.

methodsThrough exhaustive exploration of databases, such as ChEMBL, STITCH, SwissTarget- Prediction, and Gene Expression Omnibus DataSets (GEO DataSets), we have identified a comprehensive list of 46 potential targets closely related to MEP and AD. After rigorous screening using the STRING platform and Cytoscape software, we narrowed the list to nine candidate targets and ultimately identified six hub targets using three proven machine learning methods (LASSO, RF, and SVM): CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5. The "clusterProfiler" R package was used to conduct GO and KEGG enrichment analysis. At the same time, we also constructed disease prediction models for core genes. At last, six hub targets were executed molecular docking.

resultsWe derived 46 key target genes related to MEP and AD and conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. MEP might play a role in AD by affecting the pathways of neuroactive ligand-receptor interaction. Nine genes were screened as pivotal targets, followed by machine learning methods to identify six hub targets. Molecular docking analysis showed a good binding ability between MEP and CREBBP, BCL6, CXCR4, GRIN1, GOT2 and ITGA5. In addition, changes in the immune microenvironment revealed a significant impact of immune status on AD. DISCUSSIONS: This study revealed that MEP may induce AD through multiple mechanisms, such as oxidative stress, neurotoxicity, and immune regulation, and identified six core targets (CREBBP, BCL6, etc.) and found that they are related to changes in the immune microenvironment, such as T cells and B cells, providing new molecular targets for AD intervention.

conclusionOverall, CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5 have been identified as the crucial targets correlating with AD. Our findings provide a theoretical framework for understanding the complex molecular mechanisms underlying the effects of MEP on AD and provide insights for the development of prevention and treatment of AD caused by exposure to MEP.

Indexed as

Alzheimer DiseaseEnvironmental PollutantsMachine LearningMolecular Docking SimulationGene Regulatory NetworksHumansEnvironmental PollutantsAlzheimer's diseaseenvironmental pollutantsmachine learning.Methyl-4-hydroxybenzoatemolecular dockingnetwork toxicology

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.