ArticleCureus2024
Weighted Gene Co-expression Network Analysis (WGCNA) of Wnt Signaling Related to Periodontal Ligament Formation: A Bioinformatics-Based Analysis.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Key immune-related hub genes BTK, ITGAL and PTX3 mediate macrophage phenotypic shift and foam cell formation in the periodontitis-atherosclerosis axis.Molecular biology reports · 2025Article
- Integrative single-cell and multi-omics analyses reveal ferroptosis-associated gene expression and immune microenvironment heterogeneity in gastric cancer.Discover oncology · 2025Article
- Upregulated haptoglobin in classical monocytes serves as a diagnostic and immunological biomarker in myocardial infarction: a cross-sectional multi-omics study.Frontiers in immunology · 2025Article
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Authors and funding
3 authors.
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Abstract
Introduction The Wnt signaling pathway is crucial for tooth development, odontoblast differentiation, and dentin formation. It interacts with epithelial cadherin (E-cadherin) and beta-catenin in tooth development and periodontal ligament (PDL) formation. Dysregulation of Wnt signaling is linked to periodontal diseases, requiring an understanding of therapeutic interventions. Weighted gene co-expression network analysis (WGCNA) can identify co-expressed gene modules. Our study aims to identify hub genes in WGCNA analysis of Wnt signaling-based PDL formation. Methods The study used a microarray dataset GSE201313 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus to analyze the impact of DMP1 expression on XLH dental pulp cell differentiation and PDL formation. The standardized dataset was used for WGCNA analysis, which generated a co-expression network by calculating pairwise correlations between genes and constructing an adjacency matrix. The topological overlap matrix (TOM) was transformed into a hierarchical clustering tree and then cut into modules or clusters of highly interconnected genes. The module eigengene (ME) was calculated for each module, and the genes within this module were identified as hub genes. Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) was used for WGCNA analysis. Results The study used the WGCNA package to analyze 1,000 differentially expressed genes, constructing a gene co-expression network and generating a hierarchical clustering tree and TOM. The analysis reveals a scale-free topology fitting index R2 and mean connectivity for various soft threshold powers, with an R2 value of 5. COL6A1, MMP3, BGN, COL1A2, and FBN2 are hub genes implicated in PDL development. Conclusion The study identified key hub genes, including COL6A1, MMP3, BGN, and FBN2, crucial for PDL formation, tissue remodeling, and cell-matrix interactions, guiding future therapeutic strategies.
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