Network insights into childhood obesity: unveiling methylated-differentially expressed genes and pathways through integrative bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40423250", "kind": "science-record", "revision": 4, "content_hash": "sha256:a89b567a71329bca0d8fb9fa217a32a88e5fcb240f1820cd8435693c6e356d50", "created_at": "2026-11-01T20:30:33Z", "updated_at": "2026-11-01T22:56:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Network insights into childhood obesity: unveiling methylated-differentially expressed genes and pathways through integrative bioinformatics analysis.", "doi": "10.1530/EC-25-0049", "pmid": "40423250", "authors": ["Felipe Mateus Pellenz", "Guilherme Coutinho Kullmann Duarte", "Giovanna Câmara Giudicelli", "Thayne Woycinck Kowalski", "Taís Silveira Assmann", "Daisy Crispim"], "journal": "Endocrine connections", "year": 2025, "abstract": "Background: Childhood obesity, a global epidemic with profound impacts on physical and psychological health, remains a complex challenge with elusive underlying mechanisms. This study aimed to unravel the epigenetic landscape of this disease by identifying methylated-differentially expressed genes (MeDEGs) in childhood obesity through integrated bioinformatics approaches.\nMethods: Expression profiling (GSE9624) and methylation profiling (GSE25301, GSE27860, and GSE57484) datasets containing data on children with obesity (cases) and eutrophic children (control group) were obtained from the Gene Expression Omnibus (GEO) repository. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between the groups were identified using GEO2R. MeDEGs were identified by superimposing the lists of DEGs and DMGs. The protein-protein interaction (PPI) network was constructed using the STRING database and analyzed using Cytoscape. Topological and modular PPI network analyses were carried out using the CytoHubba and MCODE plugins, respectively. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG pathways.\nResults: A total of 70 MeDEGs were identified, including 45 hypomethylated high-expression and 25 hypermethylated low-expression genes. The PPI network highlighted three hub-bottleneck genes (CCL5, STAT1, and GATA3) and two functional modules. Overall, the 70 MeDEGs were associated with KEGG pathways related to cellular differentiation, inflammation, chemokine signaling, lipid and glucose metabolism, insulin resistance, and apoptosis.\nConclusion: This study, employing integrative bioinformatics approaches, provides insights into the methylation-mediated mechanisms contributing to childhood obesity, advancing our understanding of this multifaceted chronic disease.", "claims": [{"id": "claim-method", "statement": "In this study, we utilized GEO2R to analyze the included datasets and identify both DMGs and DEGs in samples from children with obesity compared to lean children. The Limma v.3.26.8 package (26) for R v.3.2.3 (https://www.r-project.org/) was employed to normalize DMGs corresponding to multiple probe IDs, estimating the average value of these probes as the representative expression level of a given gene.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40423250", "locator": "sections[4] \"Data processing\" paragraph 2", "quote": "In this study, we utilized GEO2R to analyze the included datasets and identify both DMGs and DEGs in samples from children with obesity compared to lean children. The Limma v.3.26.8 package (26) for R v.3.2.3 (https://www.r-project.org/) was employed to normalize DMGs corresponding to multiple probe IDs, estimating the average value of these probes as the representative expression level of a given gene."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE9624 dataset (GLP570; Affymetrix Human Genome U133 Plus 2.0 Array) analyzed gene expression in the visceral adipose tissue (VAT) from six lean children (five male/one female; mean age ± SD: 8.2 ± 0.6; mean BMI ± SD: 16.8 ± 0.6) and five male children with obesity (mean age: 10.2 ± 0.6; mean BMI: 28.1 ± 0.7) (21).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40423250", "locator": "sections[3] \"Microarray data\" paragraph 4", "quote": "The GSE9624 dataset (GLP570; Affymetrix Human Genome U133 Plus 2.0 Array) analyzed gene expression in the visceral adipose tissue (VAT) from six lean children (five male/one female; mean age ± SD: 8.2 ± 0.6; mean BMI ± SD: 16.8 ± 0.6) and five male children with obesity (mean age: 10.2 ± 0.6; mean BMI: 28.1 ± 0.7) (21)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The integrated analysis identified 70 methylation-regulated differentially expressed genes in childhood obesity.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40423250", "locator": "sections[9] \"Identification of DMGs and DEGs in childhood obesity\" paragraph 2", "quote": "Moreover, upon combining all four datasets, we identified 45 hypomethylated high-expression (Fig. 2A) and 25 hypermethylated low-expression genes (Fig. 2B) shared by at least one DEG/DMG dataset, totalizing 70 MeDEGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The integrated analysis identified 70 methylation-regulated differentially expressed genes in childhood obesity.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40423250"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE25301"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE25301"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40423250", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1530/EC-25-0049", "sha256": "sha256:9436ea8fdb4e293f3a1ad0cd8c39690cecd6613dec54f38c9db9f56d2333ef48", "bytes": 46410, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1530/EC-25-0049", "version": null, "note": "Full article fetched from PMC; 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