Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis - Yu, Wuhan; Yu, Weihua; Yang, Yan; Lü, Yang (Frontiers in Aging Neuroscience, 2021)
Background: Alzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects. Method: We identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted the Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed protein-protein interaction (PPI) network and utilized Cytoscape to identify hub genes. The LASSO logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes. Result: We identified 608 consensus DEGs, several dysregulated pathways and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high AUC value in both the validation dataset and combined dataset (AUC=0.992 and AUC=0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. BDNF and WWTR1, which are associated with the Braak stage, Aβ42 levels and β-secretase activity, were identified as critical genes of AD. Conclusion: Our study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.