Integrated analysis and identification of hub genes as novel biomarkers for Alzheimer’s disease

• Published in: Frontiers in aging neuroscience Vol. 14; p. 901972
• Main Authors: Zhao, Kun, Zhang, Hui, Wu, Yinyan, Liu, Jianzhi, Li, Xuezhong, Lin, Jianyang
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 30.08.2022; Frontiers Media S.A
• Subjects: AGAP3; Alzheimer's disease; Alzheimer’s disease (AD); Arteriosclerosis; Biomarkers; CD4 antigen; Cognitive ability; Datasets; Dementia; DNA microarrays; Gene expression; Genomes; hub gene; immune; Immunological memory; integrated analysis; Leukocytes (eosinophilic); Leukocytes (neutrophilic); Lung diseases; Lymphocytes B; Lymphocytes T; Macrophages; Mast cells; Memory; Memory cells; Monocytes; Natural killer cells; Neurodegenerative diseases; Neuroscience; novel biomarker; Ontology; Oxidative phosphorylation; Phosphorylation; Proteins; Random variables; Regression analysis; Reproductive system; Short term memory; Statistical methods
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2022.901972

Alzheimer’s disease (AD) is an intractable and progressive neurodegenerative disorder that can lead to severe cognitive decline, impaired speech, short-term memory loss, and finally an inability to function in daily life. For patients, their families, and even all of society, AD can impart great emotional pressure and economic costs. Therefore, this study aimed to investigate potential diagnostic biomarkers of AD. Using the Gene Expression Omnibus (GEO) database, the expression profiles of genes were extracted from the GSE5281, GSE28146, and GSE48350 microarray datasets. Then, immune-related genes were identified by the intersections of differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA), were performed. Subsequently, random forest models and least absolute shrinkage and selection operator regression were used to further screen hub genes, which were then validated using receiver operating characteristic (ROC) curve analysis. Finally, 153 total immune-related DEGs were identified in relation to AD. DO analysis of these immune-related DEGs showed that they were enriched in “lung disease,” “reproductive system disease,” and “atherosclerosis.” Single GSEA of hub genes showed that they were particularly enriched in “oxidative phosphorylation.” ROC analysis of AGAP3 yielded an area under the ROC curve of 0.878 for GSE5281, 0.727 for GSE28146, and 0.635 for GSE48350. Moreover, immune infiltration analysis demonstrated that AGAP3 was related to follicular helper T cells, naïve CD4 T cells, naïve B cells, memory B cells, macrophages M0, macrophages M1, macrophages M2, resting natural killer (NK) cells, activated NK cells, monocytes, neutrophils, eosinophils, and activated mast cells. These results indicate that identifying immune-related DEGs might enhance the current understanding of the development and prognosis of AD. Furthermore, AGAP3 not only plays a vital role in AD progression and diagnosis but could also serve as a valuable target for further research on AD.