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Identification of potential biomarkers for pathogenesis of Alzheimer’s disease
Hereditas ( IF 2.7 ) Pub Date : 2021-07-05 , DOI: 10.1186/s41065-021-00187-9
Huimin Wang 1 , Xiujiang Han 2 , Sheng Gao 2
Affiliation  

Alzheimer’s disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD. GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes. There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms. In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.

中文翻译:

鉴定阿尔茨海默病发病机制的潜在生物标志物

阿尔茨海默病 (AD) 是一种极其复杂的神经退行性疾病,占所有痴呆症诊断的近 80%。由于治疗效果有限,AD患者采取可靠的预防和诊断措施势在必行。本研究旨在探索 AD 的潜在生物标志物。GSE63060 和 GSE140829 数据集是从基因表达综合 (GEO) 数据库下载的。使用limma软件包分析GSE63060中AD组与对照组之间的差异表达基因(DEG)。使用加权基因共表达网络分析 (WGCNA) 功能包分析 GSE140829 中的 mRNA 表达数据。使用STRING分析蛋白质功能连接和相互作用,并基于度和最大集团中心性(MCC)算法筛选关键基因。对关键基因进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析。AD患者和健康对照之间的GSE63060数据集中有65个DEG。在GSE140829数据集中,绿松石模块与AD的发病机制有关,其中42个基因在GSE63060数据集中也有差异表达。然后最终筛选出 RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1 和 SNRPG 8 个基因。此外,这 42 个基因在 12 个 KEGG 通路和 119 个 GO 术语中显着富集。总之,RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG是AD发病机制的潜在生物标志物,未来应在AD中进一步探索。AD患者和健康对照之间的GSE63060数据集中有65个DEG。在GSE140829数据集中,绿松石模块与AD的发病机制有关,其中42个基因在GSE63060数据集中也有差异表达。然后最终筛选出 RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1 和 SNRPG 8 个基因。此外,这 42 个基因在 12 个 KEGG 通路和 119 个 GO 术语中显着富集。总之,RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG是AD发病机制的潜在生物标志物,未来应在AD中进一步探索。AD患者和健康对照之间的GSE63060数据集中有65个DEG。在GSE140829数据集中,绿松石模块与AD的发病机制有关,其中42个基因在GSE63060数据集中也有差异表达。然后最终筛选出 RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1 和 SNRPG 8 个基因。此外,这 42 个基因在 12 个 KEGG 通路和 119 个 GO 术语中显着富集。总之,RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG是AD发病机制的潜在生物标志物,未来应在AD中进一步探索。RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG,终于被筛选出来了。此外,这 42 个基因在 12 个 KEGG 通路和 119 个 GO 术语中显着富集。总之,RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG是AD发病机制的潜在生物标志物,未来应在AD中进一步探索。RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG,终于被筛选出来了。此外,这 42 个基因在 12 个 KEGG 通路和 119 个 GO 术语中显着富集。总之,RPS17、RPL26、RPS3A、RPS25、EEF1B2、COX7C、HINT1和SNRPG是AD发病机制的潜在生物标志物,未来应在AD中进一步探索。
更新日期:2021-07-05
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