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Abstract：
Owing to the heterogeneity of Alzheimer\'s disease (AD), its pathogenic mechanisms are yet to be fully elucidated. Evidence suggests an important role of metabolism in the pathophysiology of AD. Herein, we identified the metabolism-related AD subtypes and feature genes.
The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis.
Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression.
Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST.
The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis.
Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression.
Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST.
摘要：
背景：由于阿尔茨海默病（AD）的异质性，其致病机制尚未完全阐明。证据表明代谢在AD的病理生理学中起重要作用。在这里，我们确定了代谢相关的AD亚型和特征基因.
方法:AD数据集从基因表达综合数据库获得,代谢相关基因从先前发表的汇编中下载。进行共有聚类以鉴定AD子类。临床特点,与代谢特征的相关性，评估AD亚类的免疫浸润。使用加权相关网络分析（WGCNA）筛选特征基因，并通过基因本体论和京都基因和基因组途径分析进行处理。此外，使用三种机器学习算法来缩小特征基因的选择范围。最后,我们使用AD数据集和定量逆转录聚合酶链反应(qRT-PCR)分析鉴定了特征基因的诊断价值和表达.
结果：确定了三个AD亚类，即代谢相关(MC)A(MCA)，MCB,和MCC子类。MCA包含与高AD进展相关的特征,并且与其他两个亚类相比可能代表高风险亚类。MCA表现出与糖酵解相关的基因的高表达，果糖,和半乳糖代谢，而与柠檬酸盐循环和丙酮酸代谢相关的基因下调，并与高免疫浸润相关。相反，MCB与柠檬酸盐周期基因相关，并表现出免疫检查点基因的表达升高。使用WGCNA，101个代谢基因被鉴定为表现出与不良AD进展的最强关联。最后,机器学习算法的应用使我们能够成功识别八个特征基因，用于开发可以为AD患者带来明显临床益处的列线图模型。如AD数据集和qRT-PCR分析所示，这些基因与AD进展密切相关。
结论：代谢功能障碍与AD相关。基于代谢基因的AD假设分子亚类可能为开发AD的个体化治疗提供新的见解。与AD进展高度相关的特征基因包括GFAP，CYB5R3,DARS,KIAA0513,EZR,KCNC1、COLEC12和TST。
方法:AD数据集从基因表达综合数据库获得,代谢相关基因从先前发表的汇编中下载。进行共有聚类以鉴定AD子类。临床特点,与代谢特征的相关性，评估AD亚类的免疫浸润。使用加权相关网络分析（WGCNA）筛选特征基因，并通过基因本体论和京都基因和基因组途径分析进行处理。此外，使用三种机器学习算法来缩小特征基因的选择范围。最后,我们使用AD数据集和定量逆转录聚合酶链反应(qRT-PCR)分析鉴定了特征基因的诊断价值和表达.
结果：确定了三个AD亚类，即代谢相关(MC)A(MCA)，MCB,和MCC子类。MCA包含与高AD进展相关的特征,并且与其他两个亚类相比可能代表高风险亚类。MCA表现出与糖酵解相关的基因的高表达，果糖,和半乳糖代谢，而与柠檬酸盐循环和丙酮酸代谢相关的基因下调，并与高免疫浸润相关。相反，MCB与柠檬酸盐周期基因相关，并表现出免疫检查点基因的表达升高。使用WGCNA，101个代谢基因被鉴定为表现出与不良AD进展的最强关联。最后,机器学习算法的应用使我们能够成功识别八个特征基因，用于开发可以为AD患者带来明显临床益处的列线图模型。如AD数据集和qRT-PCR分析所示，这些基因与AD进展密切相关。
结论：代谢功能障碍与AD相关。基于代谢基因的AD假设分子亚类可能为开发AD的个体化治疗提供新的见解。与AD进展高度相关的特征基因包括GFAP，CYB5R3,DARS,KIAA0513,EZR,KCNC1、COLEC12和TST。
