Exploring the common pathogenesis of Alzheimer's disease and type 2 diabetes mellitus via microarray data analysis

by Selleckchem | Jul 14 2023

Background: Alzheimer's Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.

Purpose: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.

Methods: Download the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein-protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).

Results: The subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.

Conclusion: The common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases.

Comments:

The purpose of the study was to investigate the shared pathophysiological mechanisms between Alzheimer's Disease (AD) and Type 2 Diabetes Mellitus (DM). The researchers downloaded the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. They identified common differentially expressed genes (DEGs) using the limma package for analysis.

Several methods were employed in the study to explore the interrelations between AD and DM. These included gene functional annotation, protein-protein interaction (PPI) network analysis, potential drug screening, immune cell infiltration analysis, identification and validation of hub genes, and prediction of transcription factors (TFs).

The results of the study revealed 339 common DEGs between AD and DM. Functional analysis highlighted the importance of immunity, hormones, cytokines, neurotransmitters, and insulin in both diseases. Additionally, pathways such as serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis were found to be closely related to both AD and DM.

The researchers used the DEGs to screen small molecule compounds in the CMap database that had the potential to reverse the pathological functions of AD and DM. L-690488, exemestane, and BMS-345541 were the top-ranked compounds identified.

Furthermore, using cytoHubba, the researchers identified 10 essential hub genes, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. Among these, RAPGEF3 showed a significant positive association with AD, while NF1 showed a significant negative association with AD. ADCY5 and NF1 were also found to have significant correlations with DM phenotypes. Other datasets confirmed that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers for DM complicated with AD.

The study also explored the immune cell infiltration scores, which reflected the different cellular immune microenvironments between the two diseases.

In conclusion, this research revealed common pathogenic mechanisms between AD and DM. The identified pathways, hub genes, and potential drug candidates provide directions for further exploration of the pathogenesis and treatment of these two diseases.