Huimin Wang1, Yanqiu Zhang1, Chengyao Zheng1, Songqi Yang1, Xiuju Chen1, Heng Wang2, Sheng Gao3,4. 1. Department of Neurology, Tianjin NanKai Hospital, Tianjin, China. 2. Department of Neurology, Tianjin NanKai Hospital, Tianjin, China, sjnkyysjnk@21cn.com. 3. Department of General Practice, Tianjin NanKai Hospital, Tianjin, China. 4. Nankai University, Tianjin, China.
Abstract
BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. METHODS: All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. RESULTS: Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. CONCLUSION: The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.
BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. METHODS: All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. RESULTS: Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. CONCLUSION: The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.