Weighted Gene Co-expression Network Analysis of the Inflammatory Wnt Signaling Reveals Biomarkers Related to Bone Formation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39081453", "kind": "science-record", "revision": 3, "content_hash": "sha256:351106326c46f46865022e47e955aa23ac52909aa0ac5d225cab8e6641c34214", "created_at": "2026-11-01T20:29:09Z", "updated_at": "2026-11-01T22:12:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Weighted Gene Co-expression Network Analysis of the Inflammatory Wnt Signaling Reveals Biomarkers Related to Bone Formation.", "doi": "10.7759/cureus.63510", "pmid": "39081453", "authors": ["Pradeep Kumar Yadalam", "Ramya Ramadoss", "Ramya Suresh"], "journal": "Cureus", "year": 2024, "abstract": "Background and aim Osteocytes regulate bone metabolism and balance through various mechanisms, including the Wnt (Wingless-related integration site signal transduction) signaling pathway. Weighted gene co-expression network analysis (WGCNA) is a computational method to identify functionally related genes based on expression patterns, especially in the Wnt-beta-catenin and osteo-regenerative pathways. This study aims to analyze gene modules of the Wnt signaling pathway from WGCNA analysis. Methods The study used a microarray dataset from the GEO (GSE228306) to analyze differential gene expression in human primary monocytes. The study standardized datasets using Robust Multi-Array Average (RMA) expression measure and Integrated Differential Expression and Pathway (IDEP) analysis tool, building a co-expression network for group-specific component (GC) genes. Results The study uses WGCNA to identify co-expression modules with dysregulated mRNAs, revealing enrichment in Wnt-associated pathways and top hub-enriched genes like colony-stimulating factor 3 (CSF3), interleukin-6 (IL-6), IL-23 subunit alpha (IL23A), suppressor of cytokine signaling 1 (SOCS1), and C-C motif chemokine ligand 19 (CCL19). Conclusion WGCNA analysis of the Wnt signaling pathway will involve functional annotation, network visualization, validation, integration with other omics data, and addressing method limitations for better understanding.", "claims": [{"id": "claim-method", "statement": "Using WGCNA, the study generated a co-expression network for group-specific component (GC) genes by converting gene expression matrices into adjacency matrices, topological overlap and dissimilarity topological overlap matrices (TOMs), and mRNA similarity matrices.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39081453", "locator": "sections[2] \"Materials and methods\" paragraph 7", "quote": "Using WGCNA, the study generated a co-expression network for group-specific component (GC) genes by converting gene expression matrices into adjacency matrices, topological overlap and dissimilarity topological overlap matrices (TOMs), and mRNA similarity matrices."}], "state": "complete"}, {"id": "claim-data-access", "statement": "One microarray dataset (GSE228306) from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus [13] was retrieved. 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