ArticleScientific reports2023
A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Association Between CDH1 Downregulation and Lymphocyte Cell-Cycle Dysfunction in Alzheimer's Disease and Mild Cognitive Impairment.Cellular and molecular neurobiology · 2026Article
- Elucidating the role of SF3B3 in coronary atherosclerosis: integrating bioinformatics and machine learning for advanced insights.Journal of cardiothoracic surgery · 2026Article
- Identification of key genes related to glutamine metabolism in diabetic nephropathy by machine learning methods.Scientific reports · 2025Article
- Integrative gene co-expression network analysis reveals protein-coding and LncRNA genes associated with Alzheimer's disease pathology.Scientific reports · 2025Article
- Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and glutamine metabolism-associated gene.Journal of cardiothoracic surgery · 2025Article
- Parkinson's disease: an integrative bioinformatics and machine learning analysis reveals tryptophan metabolism-associated gene biomarkers.BMC neurology · 2025Article
- Fatty acid degradation-related gene signatures as biomarkers for ocular sarcoidosis.Scientific reports · 2025Article
- Elucidating the role of LGALS3BP in coronary atherosclerosis: integrating bioinformatics and machine learning for advanced insights.Journal of cardiothoracic surgery · 2025Article
- Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation.BMC pharmacology & toxicology · 2025Article
- Gluconeogenesis related gene signatures as biomarkers for nonspecific orbital inflammation.Scientific reports · 2025Article
- Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding.Journal of cardiothoracic surgery · 2025Article
- Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.Scientific reports · 2025Article
- Development and validation of potential molecular subtypes and signatures of thyroid eye disease based on angiogenesis-related gene analysis.BMC pharmacology & toxicology · 2025Article
- Molecular insights into ulcerative colitis and orbital inflammation.Scientific reports · 2025Article
- Investigating the neuroprotective effects ofHeliyon · 2025Article
- Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.BMC pharmacology & toxicology · 2025Article
- Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.Scientific reports · 2025Article
- Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis.Open medicine (Warsaw, Poland) · 2025Article
- Immune Regulation and ECM-Related Pathway Enrichment Reveal ATP2A3 as a Prognostic Biomarker for Nonspecific Orbital Inflammation: An Integrated Machine Learning and Mendelian Randomization Analysis.Mediators of inflammation · 2025Article
- Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.Scientific reports · 2024Article
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8 authors.
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Abstract
Alzheimer's disease (AD) is characterized as a distinct onset and progression of cognitive and functional decline associated with age, as well as a specific neuropathology. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. However, a full investigation of its role in Alzheimer's disease is still missing. This study intended to find and confirm potential Gln-related genes associated with AD using bioinformatics analysis. The discovery of GlnMgs was made possible by the intersection of the WGCNA test and 26 Gln-metabolism genes (GlnMgs). GlnMgs' putative biological functions and pathways were identified using GSVA. The LASSO method was then used to identify the hub genes as well as the diagnostic efficiency of the four GlnMgs in identifying AD. The association between hub GlnMgs and clinical characteristics was also studied. Finally, the GSE63060 was utilized to confirm the levels of expression of the four GlnMgs. Four GlnMgs were discovered (ATP5H, NDUFAB1, PFN2, and SPHKAP). For biological function analysis, cell fate specification, atrioventricular canal development, and neuron fate specification were emphasized. The diagnostic ability of the four GlnMgs in differentiating AD exhibited a good value. This study discovered four GlnMgs that are linked to AD. They shed light on potential new biomarkers for AD and tracking its progression.
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