Integrated Analysis of Genes Related to Alzheimer’s Disease Pathogenesis Using Gene Co-expression Networks

Abstract Background: This study aims to identify key molecular targets in Alzheimer’s disease (AD) occurrence and progression.Methods: GSE5281 was obtained by screening and collection of the GEO database and denoted as set 1. Differential analysis was carried out on AD samples and healthy samples in set 1. Hippocampal tissues which were extracted from APP/PS1 double transgenic mice were used to measure the syntaxin 17 (STX17) gene and protein expression. Set 1 samples was divided into the STX17 high expression group and the low expression group and denoted as set 2. Differential analysis was carried out on set 2. The hippocampal sample expression matrix in GSE48350 was named as set 3 for weighted gene co-expression network analysis (WGCNA). GSE33000 was used to construct the Least absolute shrinkage and selection operator (LASSO) model for analysis and validation. Results: 6151 differentially expressed genes (DEGs) were obtained in set 1. STX17 has significantly low expression in the hippocampal tissues of AD mice. 3651 DEGs were obtained from set 2. 2658 common DEGs were obtained in the overlap of the two sets. 22 co-expression modules were obtained from set 3. 401 genes were ultimately obtained from the overlap between genes in the WGCNA significance module and the common DEGs in the two sets and Cytoscape was used for further visualization analysis to obtain the PPI networks of 18 crucial genes. LASSO model construction and fitting was carried out to obtain seven genes common to AD and STX17 (AMPH, GAD2, GAP43, REPS2, SGIP1, STXBP1, SYN2).Conclusion: Bioinformatics analysis was used to examine the intrinsic mechanisms of AD pathogenesis and determine the important role of STX17 in AD progression. Integrated analysis was used to obtain crucial molecular targets common to AD and STX17, which provides new ideas for future AD mechanistic studies and treatment.