阿尔茨海默病RNA测序生物信息学分析及关键基因预测

Bioinformatics analysis and hub genes prediction of Alzheimer's disease RNA sequencing data

摘要:

目的： 通过转录组测序数据的分析，寻找与阿尔茨海默病（Alzheimer's disease，AD）发病有关的关键功能基因。方法： 从GEO数据库下载GSE125050 RNA测序数据，对数据进行质控和过滤后使用edgeR进行基因差异表达分析，使用Matescape数据库对差异表达基因（differentially expressed genes，DEGs）进行GO和KEGG富集分析；使用clusterProfiler对生物学意义显著的分组进行基因集富集分析（GSEA）；使用STRING数据库对神经元的DEGs进行蛋白质－蛋白质相互作用网络进行分析，从蛋白质层面筛选核心基因。结果： 差异分析中神经元、髓样细胞、星型胶质细胞、内皮细胞的样本差异表达基因数目分别为561、491、223、3 072，神经元的DEGs与Gα信号传递、角质化作用有关，皮质区细胞的DEGs显著富集在嗅觉信号通路，GSEA富集分析发现神经元表达差异与免疫激活相关，在PPI网络筛选中发现TLR8、FCGR2A、CD19、LCK、CD2、CD40、ITGAM等枢纽基因位于核心调控地位。结论： TLR8、FCGR2A、CD19、LCK、CD2、CD40、ITGAM可能与AD在免疫、神经炎症方面相关，可能在AD疾病发生发展中扮演重要角色。

Abstract:

Objective: To uncover the key genes and biological process of Alzheimer's disease by RNA sequencing data analysis. Methods: Initially, GSE125050 RNA sequencing data was obtained from the GEO database. After quality control and filtration, data were conducted genes differentially expression analysis by edgeR. Gene enrichment analysis of GO and KEGG were performed on differentially expression genes (DEGs) by Matescape database. Genes set enrichment analyses (GSEA) were performed on the biologically significant genes set. Protein-protein interaction network was constructed using neuron DEGs, to identify hub genes in protein level. Results: The number of differentially expression genes from neuron, myeloid, astrocyte, endothelial samples was 561, 491, 223, 3 072, respectively. Neuron's DEGs were significantly correlated with G alpha (s) signaling events and keratinization. Endothelial DEGs were significantly enriched at olfactory signaling pathway. GSEA showed neuron differential expression was related to activation of immune response. TLR8, FCGR2A, CD19, LCK, CD2, CD40, ITGAM were screened out by PPI network as hub genes. Conclusion: TLR8, FCGR2A, CD19, LCK, CD2, CD40, ITGAM may play a significant role in neuroinflammation in AD occurrence.