{"title":"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","authors":"Pengpeng Liang, Yale Wang, Jiamin Liu, Hai Huang, Yue Li, Jinhua Kang, Guiyun Li, Hongyan Wu","doi":"10.1002/iid3.70166","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":13289,"journal":{"name":"Immunity, Inflammation and Disease","volume":"13 4","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/iid3.70166","citationCount":"0","resultStr":"{\"title\":\"Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics\",\"authors\":\"Pengpeng Liang, Yale Wang, Jiamin Liu, Hai Huang, Yue Li, Jinhua Kang, Guiyun Li, Hongyan Wu\",\"doi\":\"10.1002/iid3.70166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>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.</p>\\n </section>\\n </div>\",\"PeriodicalId\":13289,\"journal\":{\"name\":\"Immunity, Inflammation and Disease\",\"volume\":\"13 4\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/iid3.70166\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Immunity, Inflammation and Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/iid3.70166\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Immunity, Inflammation and Disease","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/iid3.70166","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics
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.
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
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