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pmid:34012575
ECM2 and GLT8D2 in human pulmonary artery hypertension: fruits from weighted gene co-expression network analysis.
Zeyang Bai · Lianyan Xu · Yong Dai · Qingchen Yuan · Zihua Zhou
Journal of thoracic disease · 2021 doi:10.21037/jtd-20-3069
Paper
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Experiment
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Data
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Analysis
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Complete file
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New therapeutic strategies for PAH need to be established.\nMETHODS: Using the weighted gene coexpression network analysis (WGCNA) algorithm, we constructed a coexpression network of dataset GSE117261 from the Gene Expression Omnibus (GEO) database. Key modules were identified by the relationship between module eigengenes and clinical traits. Hub genes were screened out based on gene significance (GS), module membership (MM), and mean pulmonary artery pressure (mPAP). External validations were conducted in GSE48149 and GSE113439. Functional enrichment and immune cell infiltration were analyzed using Metascape and CIBERSORTx.\nRESULTS: The WGCNA analysis revealed 13 coexpression modules. The pink module had the highest correlation with PAH in terms of module eigengene (r=0.79; P=2e-18) and module significance (MS =0.43). Functional enrichment indicated genes in the pink module contributed to the immune system process and extracellular matrix (ECM). In the pink module, ECM2 (GS =0.65, MM =0.86, ρ=0.407, P=0.0019) and GLT8D2 (GS =0.63, MM =0.85, ρ=0.443, P=0.006) were identified as hub genes. For immune cells infiltration in PAH lung tissue, hub genes were positively correlated with M2 macrophages and resting mast cells, and were negatively correlated with monocytes, neutrophils, and CD4-naïve T cells.\nCONCLUSIONS: Our research identified 2 hub genes ECM2 and GLT8D2 related to PAH. The functions of these hub genes were involved in the immune process and ECM, indicating that they might serve as candidate therapeutic targets for PAH.", "claims": [{"id": "claim-method", "statement": "Using the weighted gene coexpression network analysis (WGCNA) algorithm, we constructed a coexpression network of dataset GSE117261 from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34012575", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using the weighted gene coexpression network analysis (WGCNA) algorithm, we constructed a coexpression network of dataset GSE117261 from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The dataset GSE117261 was selected to carry out the WGCNA algorithm. 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