ArticleFrontiers in immunology2022
Identification of diagnostic genes for both Alzheimer's disease and Metabolic syndrome by the machine learning algorithm.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed, 78 citations in OpenAlex.
- C1QB-Mediated Immunopathology in a Murine Malaria Model: A Multi-Omics Validation for Diagnostic and Therapeutic Targeting.International journal of molecular sciences · 2026Article
- Metabolic Syndrome and Dementia Risk in Older Adults: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- AI-Enabled Modeling for Alzheimer's Disease Risk Prediction and Validation.Revista de neurologia · 2026Article
- Multilayer Proteome and Metabolome-Based Validation Uncovers Combined Regulatory Roles and Predictive Values of 6 RNA Modifications and Cellular Senescence in Alzheimer's Disease.CNS neuroscience & therapeutics · 2026Article
- Exploring the Interconnections Between Mitochondrial Dysfunction and Polycystic Ovary Syndrome: A Comprehensive Integrated Analysis.Biochemical genetics · 2026Article
- The Mechanism of Gut Microbiota in Breast Cancer Based on the Bulk Transcriptome, Mendelian Randomization Analysis and Single Cell RNA Sequencing.MicrobiologyOpen · 2026Article
- Article
- Analysis of PANoptosis-related genes in septic cardiomyopathy by bioinformatics, machine learning and experimental validation.Experimental and therapeutic medicine · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- A machine learning-enabled blood transcriptomic signature for digital diagnosis and subtyping of Alzheimer's disease.NPJ digital medicine · 2026Article
- Deciphering MMRN1 diagnostic and therapeutic implications in the substantia nigra of Parkinson's disease patients via integrative bioinformatic analysis and multi-omics studies.Frontiers in aging neuroscience · 2026Article
- Analysis of the Genetic Comorbid Mechanisms of Type 2 Diabetes, Alzheimer's Disease, and Hypertension Using Network Modularization.BioMed research international · 2026Article
- UQCRB regulates mitochondrial energy metabolism by promoting COX5 A in atherosclerotic endothelial dysfunction.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Identification of diagnostic biomarkers and mitochondrial metabolic characteristics in sepsis-associated acute kidney injury.European journal of medical research · 2025Article
- Beta cells to brain cells: The pivotal role of insulin and glucose metabolism in Alzheimer's disease.Neuroprotection (Chichester, England) · 2025Review
- HucMSCs-Derived Extracellular Vesicles Deliver RPS27A Protein to Manipulate the MDM2-P53 Axis and Ameliorate Neurological Dysfunction in Parkinson's Disease.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2025Article
- Identification of BTK as an immune-related biomarker for Hashimoto's thyroiditis by integrated bioinformatic analysis.BMC immunology · 2025Article
- Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.Scientific reports · 2025Article
- The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers.Journal of translational medicine · 2025Article
- Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.Combinatorial chemistry & high throughput screening · 2025Article
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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