ArticleJournal of diabetes research2020
Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis.
Article in Journal of diabetes research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.
- A Meta-Analysis Approach to Gene Regulatory Network Inference Identifies Key Regulators of Cardiovascular Diseases.International journal of molecular sciences · 2024Pooled it
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- Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization.Diabetes & metabolism journal · 2025Article
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- Development and validation of a 16-gene T-cell- related prognostic model in non-small cell lung cancer.Frontiers in immunology · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- From Diabetes to Dementia: Identifying Key Genes in the Progression of Cognitive Impairment.Brain sciences · 2024Article
- Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis.Scientific reports · 2024Article
- Constructing a prognostic model for colon cancer: insights from immunity-related genes.BMC cancer · 2024Article
- Identification of co-expressed genes and immune infiltration features related to the progression of atherosclerosis.Journal of applied genetics · 2024Article
- Identification of the communal pathogenesis and immune landscape between viral myocarditis and dilated cardiomyopathy.ESC heart failure · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA).
methodsThe raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate < 0.05 and a log2 fold change ≥ 0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes.
resultsFourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally,
conclusionThis was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease.
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