Journal
FRONTIERS IN GENETICS
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2021.658323
Keywords
genetic correlation; Alzheimer disease; immune infiltration; bioinformatics analysis; GSEA
Categories
Funding
- Beijing Nova Program [Z171100001117092]
- Research Found of Chinese PLA General Hospital-Sixth Medical Center [CXPY201816]
- National Nature Science Foundation of China [81500669]
- National Nature Science Foundation of Beijing [7154241]
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In AD patients, there are significant differences in immune cell infiltration, with higher proportions of monocytes, M0 macrophages, and dendritic cells compared to normal controls, while the proportions of other cells are lower. Furthermore, a model based on immune cell infiltration can be used to predict AD patient diagnosis and provide a new perspective for AD treatment targets.
Background As the most common neurodegenerative disease, Alzheimer's disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration. Methods The gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further. Results There was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR. Conclusion A model based on immune cell infiltration might be used to forecast AD patients' diagnosis, and it provided a new perspective for AD treatment targets.
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