Artikel ; Online: Identification of diagnostic genes for both Alzheimer's disease and Metabolic syndrome by the machine learning algorithm.
2022 Band 13, Seite(n) 1037318
Abstract: Background: Alzheimer's disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few ...
| Abstract | Background: Alzheimer's disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studied genes that have a co-diagnostic role in both diseases. Methods: The microarray data of AD (GSE63060 and GSE63061 were merged after the batch effect was removed) and MetS (GSE98895) in the GEO database were downloaded. The WGCNA was used to identify the co-expression modules related to AD and MetS. RF and LASSO were used to identify the candidate genes. Machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration MetS and AD samples and to investigate the relationship between biomarkers and infiltrating immune cells. The peripheral blood mononuclear cells (PBMCs) single-cell RNA (scRNA) sequencing data from patients with AD and normal individuals were visualized with the Seurat standard flow dimension reduction clustering the metabolic pathway activity changes each cell with ssGSEA. Results: The brown module was identified as the significant module with AD and MetS. GO analysis of shared genes showed that intracellular transport and establishment of localization in cell and organelle organization were enriched in the pathophysiology of AD and MetS. By using RF and Lasso learning methods, we finally obtained eight diagnostic genes, namely Conclusion: We identified genes with common diagnostic effects on both MetS and AD, and found genes involved in multiple metabolic pathways associated with various immune cells. |
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| Mesh-Begriff(e) | Humans ; Aged ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Metabolic Syndrome/genetics ; Neurodegenerative Diseases ; Leukocytes, Mononuclear/metabolism ; Algorithms ; Machine Learning ; Biomarkers ; snRNP Core Proteins |
| Chemische Substanzen | Biomarkers ; SNRPG protein, human ; snRNP Core Proteins |
| Sprache | Englisch |
| Erscheinungsdatum | 2022-11-02 |
| Erscheinungsland | Switzerland |
| Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2606827-8 |
| ISSN | 1664-3224 ; 1664-3224 |
| ISSN (online) | 1664-3224 |
| ISSN | 1664-3224 |
| DOI | 10.3389/fimmu.2022.1037318 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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