Yixin Chen

Fudan University, China

Identification of the Shared Gene Signatures Between Alzheimer's Disease and Diabetes-Associated Cognitive Dysfunction by Bioinformatics Analysis Combined with Biological Experiment

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Biography

Yixin Chen is affiliated with the Department of Geriatrics at Huadong Hospital, Shanghai Medical College, Fudan University. Her primary research focus is on cognitive impairment, with a particular interest in early diagnosis, prevention strategies, and the management of age-related cognitive decline. She is committed to advancing clinical and academic understanding in the field of geriatric neurology and improving quality of life for older adults through evidence-based care.

Abstract

Background: Increasing evidence shows that diabetes-associated cognitive dysfunction (DACD) is closely related to the development of Alzheimer’s disease (AD). Although growing evidence supports that there is complex connection between DACO and AD, the relationship at the genetic and molecular levels remains unclear. 

Methods: The microarray data from Gene Expression Omnibus (GEO) were analyzed to screen differentially expressed genes (DEGs) of DACO and AD datasets. These DEGs were analyzed by weighted gene co-expression network analysis (WGCNA) to identify the modules and genes, respectively. Then, Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed on the common genes existing in the AD and DACO related modules by clusterProfiler and DOSE R software package. Finally, another two independent datasets were applied to confirm the results. We validated the genes screened by bioinformatics tools through molecular biology techniques.

Results: We found that 6 hub genes (GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2) were the most significant with AD and DACO, respectively. Functional enrichment analysis showed that AD and DACO common genes were mainly enriched in signalling pathways such as synaptic vesicle cycle and GABAergic synapse. After culturing HT22 cells under high glucose and amyloid-25–35 (A?25-35) conditions, we establish DACD and AD models which is used to further detect the expression levels of hub genes, and the experimental results are consistent with the analysis of the bioinformatics tools.

Conclusion: The study confirmed hub genes (GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2) that related to AD and DACO, which may provide not only new insights into the pathogenesis of AD and DACO, but also novel targets for diagnosis and treatment of AD and DACO.