{"title":"解码痴呆机制:通过整合生物信息学和机器学习鉴定关键少突胶质细胞相关基因。","authors":"Yan Chen, Hao Wen, Xinyi Qiu, Chen Li, Yinhui Yao, Yazhen Shang","doi":"10.2174/0115680266384153250804110312","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This study aims to elucidate the mechanisms underlying Dementia using bioinformatics analysis and machine learning algorithms, to identify novel therapeutic targets for its clinical management.</p><p><strong>Methods: </strong>Gene expression datasets related to dementia were sourced from the GEO database. Differentially expressed genes (DEGs) were identified using R, and key module genes were determined through the Weighted Gene Co-expression Network Analysis (WGCNA) method. Oligodendrocyte (OL) related targets were retrieved from the GeneCards database. The intersecting genes from DEGs, WGCNA, and OL were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Subsequently, three machine learning algorithms were employed to pinpoint core genes associated with OL in dementia. The CIBERSORT algorithm was used to evaluate the abundance of 22 immune cell types and their correlation with Dementia-related immune infiltration. Validation was carried out via quantitative reverse transcription polymerase chain reaction (RT-qPCR).</p><p><strong>Results: </strong>Through bioinformatics and machine learning techniques, six core OL genes associated with Dementia were identified, notably C1QA, CD163, and TGFB2, which showed elevated expression in Dementia. Immune cell infiltration analysis indicated that several immune cell types may contribute to Dementia's pathogenesis, and RT-qPCR results corroborated the bioinformatics findings.</p><p><strong>Discussion: </strong>The discovered genes may contribute to dementia pathogenesis through oligodendrocyte dysfunction and neuroimmune interactions. Notably, TGFB2 and complement-related genes (C1QA, CD163) suggest involvement in both myelination defects and neuroinflammation, highlighting their therapeutic potential.</p><p><strong>Conclusion: </strong>The six feature genes: TGFB2, C1QA, CD163, ACTG1, WIF1, and OPALIN are significantly linked to Dementia.</p>","PeriodicalId":11076,"journal":{"name":"Current topics in medicinal chemistry","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte-Associated Genes through Integrative Bioinformatics and Machine Learning.\",\"authors\":\"Yan Chen, Hao Wen, Xinyi Qiu, Chen Li, Yinhui Yao, Yazhen Shang\",\"doi\":\"10.2174/0115680266384153250804110312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>This study aims to elucidate the mechanisms underlying Dementia using bioinformatics analysis and machine learning algorithms, to identify novel therapeutic targets for its clinical management.</p><p><strong>Methods: </strong>Gene expression datasets related to dementia were sourced from the GEO database. Differentially expressed genes (DEGs) were identified using R, and key module genes were determined through the Weighted Gene Co-expression Network Analysis (WGCNA) method. Oligodendrocyte (OL) related targets were retrieved from the GeneCards database. The intersecting genes from DEGs, WGCNA, and OL were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Subsequently, three machine learning algorithms were employed to pinpoint core genes associated with OL in dementia. The CIBERSORT algorithm was used to evaluate the abundance of 22 immune cell types and their correlation with Dementia-related immune infiltration. Validation was carried out via quantitative reverse transcription polymerase chain reaction (RT-qPCR).</p><p><strong>Results: </strong>Through bioinformatics and machine learning techniques, six core OL genes associated with Dementia were identified, notably C1QA, CD163, and TGFB2, which showed elevated expression in Dementia. Immune cell infiltration analysis indicated that several immune cell types may contribute to Dementia's pathogenesis, and RT-qPCR results corroborated the bioinformatics findings.</p><p><strong>Discussion: </strong>The discovered genes may contribute to dementia pathogenesis through oligodendrocyte dysfunction and neuroimmune interactions. Notably, TGFB2 and complement-related genes (C1QA, CD163) suggest involvement in both myelination defects and neuroinflammation, highlighting their therapeutic potential.</p><p><strong>Conclusion: </strong>The six feature genes: TGFB2, C1QA, CD163, ACTG1, WIF1, and OPALIN are significantly linked to Dementia.</p>\",\"PeriodicalId\":11076,\"journal\":{\"name\":\"Current topics in medicinal chemistry\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current topics in medicinal chemistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/0115680266384153250804110312\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current topics in medicinal chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0115680266384153250804110312","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte-Associated Genes through Integrative Bioinformatics and Machine Learning.
Introduction: This study aims to elucidate the mechanisms underlying Dementia using bioinformatics analysis and machine learning algorithms, to identify novel therapeutic targets for its clinical management.
Methods: Gene expression datasets related to dementia were sourced from the GEO database. Differentially expressed genes (DEGs) were identified using R, and key module genes were determined through the Weighted Gene Co-expression Network Analysis (WGCNA) method. Oligodendrocyte (OL) related targets were retrieved from the GeneCards database. The intersecting genes from DEGs, WGCNA, and OL were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Subsequently, three machine learning algorithms were employed to pinpoint core genes associated with OL in dementia. The CIBERSORT algorithm was used to evaluate the abundance of 22 immune cell types and their correlation with Dementia-related immune infiltration. Validation was carried out via quantitative reverse transcription polymerase chain reaction (RT-qPCR).
Results: Through bioinformatics and machine learning techniques, six core OL genes associated with Dementia were identified, notably C1QA, CD163, and TGFB2, which showed elevated expression in Dementia. Immune cell infiltration analysis indicated that several immune cell types may contribute to Dementia's pathogenesis, and RT-qPCR results corroborated the bioinformatics findings.
Discussion: The discovered genes may contribute to dementia pathogenesis through oligodendrocyte dysfunction and neuroimmune interactions. Notably, TGFB2 and complement-related genes (C1QA, CD163) suggest involvement in both myelination defects and neuroinflammation, highlighting their therapeutic potential.
Conclusion: The six feature genes: TGFB2, C1QA, CD163, ACTG1, WIF1, and OPALIN are significantly linked to Dementia.
期刊介绍:
Current Topics in Medicinal Chemistry is a forum for the review of areas of keen and topical interest to medicinal chemists and others in the allied disciplines. Each issue is solely devoted to a specific topic, containing six to nine reviews, which provide the reader a comprehensive survey of that area. A Guest Editor who is an expert in the topic under review, will assemble each issue. The scope of Current Topics in Medicinal Chemistry will cover all areas of medicinal chemistry, including current developments in rational drug design, synthetic chemistry, bioorganic chemistry, high-throughput screening, combinatorial chemistry, compound diversity measurements, drug absorption, drug distribution, metabolism, new and emerging drug targets, natural products, pharmacogenomics, and structure-activity relationships. Medicinal chemistry is a rapidly maturing discipline. The study of how structure and function are related is absolutely essential to understanding the molecular basis of life. Current Topics in Medicinal Chemistry aims to contribute to the growth of scientific knowledge and insight, and facilitate the discovery and development of new therapeutic agents to treat debilitating human disorders. The journal is essential for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important advances.