Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41357074", "kind": "science-record", "revision": 4, "content_hash": "sha256:1f178825f81bbdf32295b6bc2f96b880f5b69f5400cf86206ed1c1b620f3c935", "created_at": "2026-11-01T20:31:22Z", "updated_at": "2026-11-01T22:57:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.", "doi": "10.3389/fcvm.2025.1694255", "pmid": "41357074", "authors": ["Linyuan Wang", "Kun Yang", "Ruilong Kang", "Pengbo Liu", "Yongzhi Deng"], "journal": "Frontiers in cardiovascular medicine", "year": 2025, "abstract": "Background: Hyperthyroidism and atrial fibrillation (AF) are interrelated conditions with significant cardiovascular impact. While their clinical association is established, the molecular mechanisms remain unclear. Identifying shared biomarkers and pathways can advance understanding and guide therapy.\nMethods: The hyperthyroidism dataset GSE71956 and the AF dataset GSE115574 were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis was performed using the \"limma\" package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. To validate the predictive results, peripheral blood samples were collected for real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Finally, immune infiltration analysis was conducted to evaluate immune cell changes in hyperthyroidism and AF.\nResults: Through differential gene screening and WGCNA, 23 overlapping genes associated with hyperthyroidism and AF were identified. Using least absolute shrinkage and selection operator (LASSO) and random forest (RF) machine learning algorithms, CXCL16 and TMEM127 were ultimately identified as key genes. The two genes demonstrated good diagnostic efficacy in the hyperthyroidism validation set GSE276271 (AUC: TMEM127, 0.636; CXCL16, 0.591) and in the AF validation set GSE2240 (AUC: TMEM127, 0.745; CXCL16, 0.720). RT-qPCR analysis demonstrated that CXCL16 and TMEM127 expression levels were significantly elevated in both the hyperthyroidism and AF groups compared to the control group, aligning with the findings from our prior bioinformatics analysis. Immune analysis revealed significant differences in two immune cell types in both hyperthyroidism and AF.\nConclusion: CXCL16 and TMEM127 are promising biomarkers, offering insights into the shared pathogenesis of hyperthyroidism and AF. These findings provide a foundation for novel diagnostic and therapeutic strategies targeting these conditions.", "claims": [{"id": "claim-method", "statement": "DEGs were analyzed using the “limma” R package by comparing hyperthyroidism samples against controls in the GSE71956 dataset and AF samples against controls in the GSE115574 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41357074", "locator": "sections[4] \"Identification of differentially expressed genes (DEGs)\" paragraph 2", "quote": "DEGs were analyzed using the “limma” R package by comparing hyperthyroidism samples against controls in the GSE71956 dataset and AF samples against controls in the GSE115574 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41357074", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using R software for data analysis, we identified 1,575 DEGs in the hyperthyroidism dataset (GSE71956), comprising 527 upregulated and 1,048 downregulated genes (Figures 2A,B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41357074", "locator": "sections[13] \"Identification of DEGs\" paragraph 2", "quote": "Using R software for data analysis, we identified 1,575 DEGs in the hyperthyroidism dataset (GSE71956), comprising 527 upregulated and 1,048 downregulated genes (Figures 2A,B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using R software for data analysis, we identified 1,575 DEGs in the hyperthyroidism dataset (GSE71956), comprising 527 upregulated and 1,048 downregulated genes (Figures 2A,B).", "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:41357074"], "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. 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