ArticleAnnals of translational medicine2019
The application of weighted gene co-expression network analysis in identifying key modules and hub genes associated with disease status in Alzheimer's disease.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Gene expression profiles in sporadic ALS fibroblasts define disease subtypes and the metabolic effects of the investigational drug EH301.Human molecular genetics · 2022Pooled it
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- Balanced deep learning on multi-omics networks identifies molecular subgroups of pathological brain aging.medRxiv : the preprint server for health sciences · 2026Article
- Insight into dysregulated VEGF-related genes in diabetic retinopathy through bioinformatic analyses.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway.Journal of Cancer · 2024Article
- Co-expression network analysis of frontal cortex during the progression of Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis.IET systems biology · 2022Article
- Establishing a competing endogenous RNA (ceRNA)-immunoregulatory network associated with the progression of Alzheimer's disease.Annals of translational medicine · 2022Article
- Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data.Frontiers in neurology · 2022Article
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- Disrupted balance of long and short-range functional connectivity density in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study.Annals of translational medicine · 2021Article
- Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database.BMC cardiovascular disorders · 2020Article
- Phenotypic Subtyping and Re-analyses of Existing Transcriptomic Data from Autistic Probands in Simplex Families Reveal Differentially Expressed and ASD Trait-Associated Genes.Frontiers in neurology · 2020Article
- Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer's Disease Pathogenesis.Frontiers in aging neuroscience · 2020Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is the most common neurodegenerative condition that affects more than 15 million individuals globally. However, a predictive molecular biomarker to distinguish the different stages of AD patients is still lacking.
methodsA weighted gene co-expression network analysis (WGCNA) was employed to systematically identify the co-expressed gene modules and hub genes connected with AD development based on a microarray dataset (GSE1297) from the Gene Expression Omnibus (GEO) database. An independent validation cohort, GSE28146, was utilized to assess the diagnostic efficiency for distinguishing the different stages of AD. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and western blotting analysis were applied to examine the mRNA and protein level of GRIK1, respectively, in AD mice established with the expression of mutant amyloid precursor protein and wild type mice. The morphology of neurons was investigated using phalloidin staining.
resultsWe identified 16 co-expressed genes modules, with the pink module showing significant association with all three disease statuses [neurofibrillary tangle (NFT), BRAAK, and mini-mental state examination (MMSE)]. Enrichment analysis specified that these modules were enriched in phosphatidylinositol 3-kinase (PI3K) signaling and ion transmembrane transport. The validation cohort GSE28146 confirmed that six hub genes in the pink module could distinguish severe and non-severe AD patients [area under the curve (AUC) >0.7]. These hub genes might act as a biomarker and help to differentiate diverse pathological stages for AD patients. Finally, one of the hubs, GRIK1, was validated by an animal AD model. The mRNA and protein level of GRIK1 in the AD hippocampus was increased compared with the control group (NC) hippocampus. Phalloidin staining showed that dendritic length of the GRIK1 pCDNA3.1 group was shorter than that of the NC group.
conclusionsIn summary, we systematically recognized co-expressed gene modules and genes related to AD stages, which gave insight into the fundamental mechanisms of AD progression and revealed some probable targets for the treatment of AD.
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