| Literature DB >> 34117614 |
Yafei Chen1,2, Qiong Liu2, Jun Liu2, Penglu Wei3, Bing Li4, Nongyun Wang5, Zhenquan Liu6,7, Zhong Wang2.
Abstract
Alzheimer's disease (AD), vascular dementia (VD), and Parkinson's disease (PD) exert increasingly lethal or disabling effects on humans, but the associations among these diseases at the molecular level remain unclear. In our research, lists of genes related to these three diseases were acquired from public databases. We constructed gene-gene networks of the lists of disease-related genes using the STRING database and selected the plug-in MCODE as the most suitable method to divide the three disease-associated networks into modules through an entropy calculation. Notably, 1173 AD-related, 203 VD-related, and 722 PD-related genes as well as 72 overlapping genes were observed among the three diseases. By dividing the modules from the gene network, we divided the AD-related gene network into 27 modules, the VD-related gene network into 8 modules, and the PD-related gene network into 17 modules. After the enrichment analysis of each disease-related gene, 146 overlapping biological processes and 32 overlapping pathways were identified. Ultimately, through similarity analysis of the genes, biological processes, and pathways, we found that AD and VD were the most closely related at the biological process and pathway levels, with similarity coefficients of 0.2784 and 0.3626, respectively. After analyzing the overlapping gene network, we found that INS might play an important role in the network and that insulin and its signaling pathways may play a key role in these neurodegenerative diseases. Our research illustrates a new method for in-depth research on the three diseases, which may accelerate the progress of developing new therapeutics and may be applied to prevent neurodegenerative diseases.Entities:
Keywords: Alzheimer’s disease; Enrichment analysis; Gene network; Parkinson’s disease; Therapeutic prediction; Vascular dementia
Mesh:
Year: 2021 PMID: 34117614 DOI: 10.1007/s12017-021-08670-2
Source DB: PubMed Journal: Neuromolecular Med ISSN: 1535-1084 Impact factor: 3.843