Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics

IF 3.1 4区医学 Q3 IMMUNOLOGY

Immunity, Inflammation and Disease Pub Date : 2025-04-07 DOI:10.1002/iid3.70166

Pengpeng Liang, Yale Wang, Jiamin Liu, Hai Huang, Yue Li, Jinhua Kang, Guiyun Li, Hongyan Wu

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Abstract

Background

Recent research highlights the immune system's role in AD pathogenesis and promising prospects of natural compounds in treatment. This study explores immunity-related biomarkers and potential natural products using bioinformatics, machine learning, molecular docking, and kinetic simulation.

Methods

Differentially expressed genes (DEGs) in AD were analyzed using GSE5281 and GSE132903 datasets. Important AD module genes were identified using a weighted co-expression algorithm (WGCNA), and immune-related genes (IRGs) were obtained from the ImmPortPortal database. Intersecting these genes yielded important IRGs. Then, the least absolute shrinkage and selection operator (LASSO) and other methods screened common immune-related AD markers. Biological pathways were explored through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The accuracy of these markers was assessed by subject operator signature (ROC) curves and validated in the GSE122063 dataset. The datasets was then subjected to immunoinfiltration analysis. Multiple compound databases were used to analyze core Chinese medicines and components. Molecular docking and kinetic simulation verification were used for further verification.

Results

A total of 1360 differential genes and 5 biomarkers (PGF, GFAP, GPI, SST, NFKBIA) were identified, showing excellent diagnostic efficiency. GSEA revealed markers associated with Oxidative phosphorylation, Nicotine addiction, and Hippo signaling pathway. Immune infiltration analysis showed dysregulation in multiple immune cell types in AD brains, with significant interactions between markers and 5 immune cell types. A total of 27 possible herbs and 7 core compounds were eventually identified. The binding environment of GPI-luteolin and GPI-stigasterol was relatively stable and showed good affinity.

Conclusions

PGF, GFAP, SST, GPI, and NFKBIA were identified for early AD diagnosis, associated with immune cells and pathways in AD brains. 7 promising natural compounds, including luteolin and stigmasterol, were screened for targeting these biomarkers.

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基于生物信息学、分子对接和分子动力学的阿尔茨海默病免疫相关基因和天然产物的鉴定和探索

近年来的研究强调免疫系统在阿尔茨海默病发病机制中的作用以及天然化合物治疗阿尔茨海默病的前景。本研究利用生物信息学、机器学习、分子对接和动力学模拟等方法探索免疫相关的生物标志物和潜在的天然产物。方法采用GSE5281和GSE132903数据集对AD的差异表达基因（DEGs）进行分析。使用加权共表达算法（WGCNA）鉴定重要的AD模块基因，并从importportal数据库中获得免疫相关基因（IRGs）。这些基因的交叉产生了重要的IRGs。然后采用最小绝对收缩和选择算子（LASSO）等方法筛选常见的免疫相关AD标记物。通过基因本体（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）探索生物学途径。通过受试者算子特征（ROC）曲线评估这些标记的准确性，并在GSE122063数据集中进行验证。然后对数据集进行免疫浸润分析。采用多个复方数据库对核心中药及其成分进行分析。通过分子对接和动力学模拟验证进一步验证。结果共鉴定出1360个差异基因和5个生物标志物（PGF、GFAP、GPI、SST、NFKBIA），具有较好的诊断效果。GSEA发现了与氧化磷酸化、尼古丁成瘾和Hippo信号通路相关的标志物。免疫浸润分析显示，AD脑中多种免疫细胞类型失调，标记物与5种免疫细胞类型存在显著的相互作用。最终鉴定出27种可能的草药和7种核心化合物。gpi -木犀草素和gpi -山甾醇的结合环境相对稳定，具有良好的亲和力。结论PGF、GFAP、SST、GPI和NFKBIA可用于AD早期诊断，并与AD大脑中的免疫细胞和通路相关。针对这些生物标志物，筛选了7种有前景的天然化合物，包括木犀草素和豆甾醇。

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来源期刊

Immunity, Inflammation and Disease Medicine-Immunology and Allergy

CiteScore

3.60

自引率

0.00%

发文量

146

审稿时长

8 weeks

期刊介绍： Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology