Linking Diabetes Mellitus with Alzheimer's Disease: Bioinformatics Analysis for the Potential Pathways and Characteristic Genes

Huang, Cheng; Luo, Juyu; Wen, Xueyi; Li, Keshen

doi:10.1007/s10528-021-10154-8

Cited by 14 publications

(6 citation statements)

References 84 publications

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“… Zhang et al (2023b) identified seven hub genes of co-DEGs between T2DM, MDD, and dementia. Huang C. et al (2022) confirmed through multiple comparisons that CACNA2D3, NUMB, and IER3 simultaneously participate in AD and T2D, and analyzed interacting chemicals, transcription factors, and miRNAs.…”

Section: Identification Of Shared Candidate Biomarkers Between Ad And...mentioning

confidence: 83%

A review and analysis of key biomarkers in Alzheimer’s disease

Zhang,

Liu,

Zhang

et al. 2024

Front. Neurosci.

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein–protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

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Section: Identification Of Shared Candidate Biomarkers Between Ad And...mentioning

confidence: 83%

A review and analysis of key biomarkers in Alzheimer’s disease

Zhang,

Liu,

Zhang

et al. 2024

Front. Neurosci.

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“…The “limma” package [ 22 ] (version 3.44.3) was executed to identify the differentially expressed genes (DEGs) between controls and RA tissues in the merged dataset (GSE55235 and GSE55457). Based on the calculated results, the gene set enrichment analysis (GSEA) enrichment analysis were performed through executing the clusterProfiler package (version 3.16.0) [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis

Xia

Zhang

et al. 2023

BMC Med Genomics

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Background Rheumatoid arthritis (RA) is an autoimmune disease characterized by destructive and symmetrical joint diseases and synovitis. This research attempted to explore the mechanisms involving ferroptosis in RA, and find the biological markers by integrated analysis. Methods Gene expression data (GSE55235 and GSE55457) of synovial tissues from healthy and RA individuals were downloaded. By filtering the differentially expressed genes (DEGs) and intersecting them with the 484 ferroptosis-related genes (FRGs), the overlapping genes were identified. After the enrichment analysis, the machine learning-based approaches were introduced to screen the potential biomarkers, which were further validated in other two datasets (GSE77298 and GSE93272) and cell samples. Besides, we also analyze the infiltrating immune cells in RA and their correlation with the biomarkers. Results With the criteria, 635 DEGs in RA were included, and 29 of them overlapped in the reported 484 FRGs. The enrichments of the 29 differentially expressed ferroptosis-related genes indicated that they may involve in the FoxO signaling pathway and inherited metabolic disorder. RRM2, validating by the external datasets and western blot, were identified as the biomarker with the high diagnostic value, whose associated immune cells, such as Neutrophils and Macrophages M1, were also further evaluated. Conclusion We preliminary explored the mechanisms between ferroptosis and RA. These results may help us better comprehend the pathophysiological changes of RA in basic research, and provide new evidences for the clinical transformation.

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“…For the .CEL format files (GSE161355), the “affy” [ 20 ] (version 1.66.0), and “affyPLM” [ 21 ] (version 1.64.0) packages are used to process the raw data by the RMA (robust multiarray average) function firstly [ 22 ]. Then, the probe identification numbers were converted into the official gene symbols according to the GPL570 platform.…”

Section: Methodsmentioning

confidence: 99%

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

Huang

Wen

Xie

et al. 2022

Oxidative Medicine and Cellular Longevity

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Currently, Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are widely prevalent in the elderly population, and accumulating evidence implies a strong link between them. For example, patients with T2DM have a higher risk of developing neurocognitive disorders, including AD, but the exact mechanisms are still unclear. This time, by combining bioinformatics analysis and in vivo experimental validation, we attempted to find a common biological link between AD and T2DM. We firstly downloaded the gene expression profiling (AD: GSE122063; T2DM: GSE161355) derived from the temporal cortex. To find the associations, differentially expressed genes (DEGs) of the two datasets were filtered and intersected. Based on them, enrichment analysis was carried out, and the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify the specific genes. After verifying in the external dataset and in the samples from the AD and type 2 diabetes animals, the shared targets of the two diseases were finally determined. Based on them, the ceRNA networks were constructed. Besides, the logistic regression and single-sample gene set enrichment analysis (ssGSEA) were performed. As a result, 62 DEGs were totally identified between AD and T2DM, and the enrichment analysis indicated that they were much related to the function of synaptic vesicle and MAPK signaling pathway. Based on the evidence from external dataset and RT-qPCR, CARTPT, EPHA5, and SERPINA3 were identified as the marker genes in both diseases, and their clinical significance and biological functions were further analyzed. In conclusion, discovering and exploring the marker genes that are dysregulated in both 2 diseases could help us better comprehend the intrinsic relationship between T2DM and AD, which may inspire us to develop new strategies for facing the dilemmas of clinical or basic research in cognitive dysfunction.

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Linking Diabetes Mellitus with Alzheimer's Disease: Bioinformatics Analysis for the Potential Pathways and Characteristic Genes

Cited by 14 publications

References 84 publications

A review and analysis of key biomarkers in Alzheimer’s disease

A review and analysis of key biomarkers in Alzheimer’s disease

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

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