ArticleFrontiers in aging neuroscience2022
Identification of Key Biomarkers and Pathways for Maintaining Cognitively Normal Brain Aging Based on Integrated Bioinformatics Analysis.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 22 citations in OpenAlex.
- VSIG4 Inhibits Microglia-Mediated Neuroinflammation and Neuronal Injury by Modulating the JAK2-STAT3/NF-κB Signaling Pathway in Parkinson's Disease.Molecular neurobiology · 2026Article
- Single-cell profiling identifies a pro-tumoral VCAN positive macrophage subset and defines a prognostic signature in glioblastoma.Discover oncology · 2026Article
- Proteome Profiling of Serum Reveals Pathological Mechanisms and Biomarker Candidates for Cerebral Small Vessel Disease.Translational stroke research · 2025Article
- Paracrine Bone-Derived Senescent Secretome Induces Spatially Patterned ECM and Biomechanical Vulnerability in Human Brain Organoids.bioRxiv : the preprint server for biology · 2025Article
- Dysregulations of C1QA, C1QB, C1QC and C5AR1 as candidate biomarkers of vascular dementia.npj aging · 2025Article
- Exploring the Plasma Proteome: Identifying Hub Proteins linking Aging, Homeostasis, and Organ Function.International journal of medical sciences · 2025Article
- Multiomics of Aging and Aging-Related Diseases.International journal of molecular sciences · 2024Review
- Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm.Journal of molecular neuroscience : MN · 2024Article
- The decreased astrocyte-microglia interaction reflects the early characteristics of Alzheimer's disease.iScience · 2024Article
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- Developmental dynamics of the single nucleus regulatory landscape of pig hippocampus.Science China. Life sciences · 2023Article
- DiLeu Isobaric Labeling Coupled with Limited Proteolysis Mass Spectrometry for High-Throughput Profiling of Protein Structural Changes in Alzheimer's Disease.Analytical chemistry · 2023Article
- C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke.Oxidative medicine and cellular longevity · 2023Article
- A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis.Frontiers in immunology · 2023Article
- Integrated bioinformatic analysis and experimental validation for exploring the key molecular of brain inflammaging.Frontiers in immunology · 2023Article
- Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer's disease and their immune cell infiltration correlation.Frontiers in neuroscience · 2023Article
- Exploring molecular signatures related to the mechanism of aging in different brain regions by integrated bioinformatics.Frontiers in molecular neuroscience · 2023Article
- Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer's disease.Frontiers in aging neuroscience · 2022Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Given the arrival of the aging population has caused a series of social and economic problems, we aimed to explore the key genes underlying cognitively normal brain aging and its potential molecular mechanisms. Methods: GSE11882 was downloaded from Gene Expression Omnibus (GEO). The data from different brain regions were divided into aged and young groups for analysis. Co-expressed differentially expressed genes (DEGs) were screened. Functional analysis, protein-protein interaction (PPI) network, microRNA (miRNA)-gene, and transcription factor (TF)-gene networks were performed to identify hub genes and related molecular mechanisms. AlzData database was used to elucidate the expression of DEGs and hub genes in the aging brain. Animal studies were conducted to validate the hub genes. Results: Co-expressed DEGs contained 7 upregulated and 87 downregulated genes. The enrichment analysis indicated DEGs were mainly involved in biological processes and pathways related to immune-inflammatory responses. From the PPI network, 10 hub genes were identified: C1QC, C1QA, C1QB, CD163, FCER1G, VSIG4, CD93, CD14, VWF, and CD44. CD44 and CD93 were the most targeted DEGs in the miRNA-gene network, and TIMP1, HLA-DRA, VWF, and FGF2 were the top four targeted DEGs in the TF-gene network. In AlzData database, the levels of CD44, CD93, and CD163 in patients with Alzheimer's disease (AD) were significantly increased than those in normal controls. Meanwhile, in the brain tissues of cognitively normal mice, the expression of CD44, CD93, and CD163 in the aged group was significantly lower than those in the young group. Conclusion: The underlying molecular mechanisms for maintaining healthy brain aging are related to the decline of immune-inflammatory responses. CD44, CD93, and CD 163 are considered as potential biomarkers. This study provides more molecular evidence for maintaining cognitively normal brain aging.
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