Screening and function analysis of key genes and signaling pathways in Alzheimer’s disease via next generation sequencing data analysis and bioinformatics approaches

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by extracellular senile plaques. The pathogenesis of AD remains unclear. This study aims to explore the molecular mechanism of AD through bioinformatics and next generation sequencing (NGS) data analysis. NGS dataset GSE125583 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the “limma” R bioconductor package. Gene Ontology (GO) and REACTOME enrichment analysis were conducted. Analysis and visualization of protein–protein interaction network (PPI) and modules were carried out with HIPPIE and Cytoscape. Subsequently, miRNA-hub gene regulatory network and TF-hub gene regulatory network were built to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. Finally, receiver operating characteristic (ROC) curve to assess the diagnostic efficacy of hub genes, which have been validated with training and validation datasets. Totally 956 DEGs (479 up regulated DEGs and 477 down regulated DEGs) were identified from GSE125583. GO and REACTOME pathway enrichment analysis indicated that DEGS were mainly enriched in regulation of biological quality, cell junction, channel activity, cell communication, plasma membrane, signaling receptor activity, neuronal system and GPCR ligand binding. 10 hub genes were selected, including PAK1, ELAVL2, NSF, HTR2C, TERT, UBD, MKI67, HSPB1, PYHIN1 and TES. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed successfully. Hsa-mir-4517, hsa-mir-3652, EGR1 and ZNF354C were predicted as possible key miRNAs and TFs for progression of AD. In conclusion, the results in this study provided reliable key genes and signaling pathways for AD, which will be useful for AD molecular mechanisms, diagnosis, prognosis and candidate targeted treatment.

Keywords

GEO database / Bioinformatics analysis / Alzheimer’s disease / Signaling pathways / Differently expressed genes

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RIGHTS & PERMISSIONS

Shenzhen University School of Medicine; Fondazione Istituto FIRC di Oncologia Molecolare