Zhang Fan, Zhong Siran, Yang Siman, et al. Bioinformatics analysis and hub genes prediction of Alzheimer’s disease RNA sequencing data. [J]. Journal of Chongqing Medical University 46(5):539-545(2021)
DOI:
Zhang Fan, Zhong Siran, Yang Siman, et al. Bioinformatics analysis and hub genes prediction of Alzheimer’s disease RNA sequencing data. [J]. Journal of Chongqing Medical University 46(5):539-545(2021) DOI: 10.13406/j.cnki.cyxb.002647.
Bioinformatics analysis and hub genes prediction of Alzheimer’s disease RNA sequencing data
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.