Background: Alzheimer's disease (AD), type 2 diabetes mellitus (T2DM), and Major Depressive Disorder (MDD) have a higher incidence rate in modern society. Although increasing evidence supports close associations between the three, the mechanisms underlying their interrelationships remain elucidated.

Objective: The primary purpose is to explore the shared pathogenesis and the potential peripheral blood biomarkers for AD, MDD, and T2DM.

Methods: We downloaded the microarray data of AD, MDD, and T2DM from the Gene Expression Omnibus database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis to identify differentially expressed genes. We took the intersection of differentially expressed genes to obtain co-DEGs. Then, we performed GO and KEGG enrichment analysis on the common genes in the AD, MDD, and T2DM-related modules. Next, we utilized the STRING database to find the hub genes in the protein-protein interaction network. ROC curves were constructed for co-DEGs to obtain the most diagnostic valuable genes and to make drug predictions against the target genes. Finally, we conducted a present condition survey to verify the correlation between T2DM, MDD and AD.

Results: Our findings indicated 127 diff co-DEGs, 19 upregulated co-DEGs, and 25 down-regulated co-DEGs. Functional enrichment analysis showed co-DEGs were mainly enriched in signaling pathways such as metabolic diseases and some neurodegeneration. Protein-protein interaction network construction identified hub genes in AD, MDD and T2DM shared genes. We identified seven hub genes of co-DEGs, namely, , , , , , , and . The current survey results suggest a correlation between T2DM, MDD and dementia. Moreover, logistic regression analysis showed that T2DM and depression increased the risk of dementia.

Conclusion: Our work identified common pathogenesis of AD, T2DM, and MDD. These shared pathways might provide novel ideas for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosing and treating.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040717PMC
http://dx.doi.org/10.1016/j.heliyon.2023.e14653DOI Listing

Publication Analysis

Top Keywords

hub genes
16
t2dm mdd
12
genes
10
peripheral blood
8
blood biomarkers
8
alzheimer's disease
8
major depressive
8
depressive disorder
8
type diabetes
8
mdd
8

Similar Publications

Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.

Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.

Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.

View Article and Find Full Text PDF

Background: The underlying molecular processes of atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD) are frequently linked to increased morbidity and mortality when they co-occur. However, their underlying molecular mechanisms are questioned due to their incomplete analysis.

Objective: This study aimed to identify common differentially expressed genes (DEGs) in AF and COPD patients and investigate their potential biological functions and pathways.

View Article and Find Full Text PDF

Background: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.  METHODS: In this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA.

View Article and Find Full Text PDF

Testicular germ cell tumour (TGCT) is a malignancy with known inherited risk factors, affecting young men. We have previously identified several hundred differentially abundant circulating RNAs in pre-diagnostic serum from TGCT cases compared to healthy controls. In this study, we performed Weighted Gene Co-expression Network Analysis (WGCNA) on mRNA and miRNA data from these samples.

View Article and Find Full Text PDF

Low fertility in cows leads to early removal from herds. Since reproductive traits are complex and have low heritability, genetic analysis can aid in improving reproduction. This study identified key genes linked to fertility by conducting genome- and transcriptome-wide association studies, RNA-seq analysis, meta-analysis, weighted gene co-expression network analysis, and functional enrichment analysis.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!