Transcriptional landscape of myasthenia gravis revealed by weighted gene coexpression network analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37051601", "kind": "science-record", "revision": 4, "content_hash": "sha256:d5c49202a9fabdab35a345d86afad43f70eede74eda6000d5fb05c9fa1405de4", "created_at": "2026-11-01T19:01:42Z", "updated_at": "2026-11-01T22:51:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptional landscape of myasthenia gravis revealed by weighted gene coexpression network analysis.", "doi": "10.3389/fgene.2023.1106359", "pmid": "37051601", "authors": ["Demin Zhang", "Liqin Luo", "Feng Lu", "Bo Li", "Xiaoyun Lai"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: As one of the most common autoimmune diseases, myasthenia gravis (MG) severely affects the quality of life of patients. Therefore, exploring the role of dysregulated genes between MG and healthy controls in the diagnosis of MG is beneficial to reveal new and promising diagnostic biomarkers and clinical therapeutic targets. Methods: The GSE85452 dataset was downloaded from the Gene Expression Omnibus (GEO) database and differential gene expression analysis was performed on MG and healthy control samples to identify differentially expressed genes (DEGs). The functions and pathways involved in DEGs were also explored by functional enrichment analysis. Significantly associated modular genes were identified by weighted gene co-expression network analysis (WGCNA), and MG dysregulated gene co-expression modular-based diagnostic models were constructed by gene set variance analysis (GSVA) and least absolute shrinkage and selection operator (LASSO). In addition, the effect of model genes on tumor immune infiltrating cells was assessed by CIBERSORT. Finally, the upstream regulators of MG dysregulated gene co-expression module were obtained by Pivot analysis. Results: The green module with high diagnostic performance was identified by GSVA and WGCNA. The LASSO model obtained NAPB, C5orf25 and ERICH1 genes had excellent diagnostic performance for MG. Immune cell infiltration results showed a significant negative correlation between green module scores and infiltration abundance of Macrophages M2 cells. Conclusion: In this study, a diagnostic model based on the co-expression module of MG dysregulated genes was constructed, which has good diagnostic performance and contributes to the diagnosis of MG.", "claims": [{"id": "claim-method", "statement": "LASSO regression analysis was performed using the Bioinforcloud application RunLASSO, which was developed based on the glmnet software package (Friedman et al., 2010) and extracted the expression profiles of MG dysregulated genes and co-expression module functional genes with diagnostic efficacy to construct the LASSO model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37051601", "locator": "sections[10] \"Construction of minimum absolute shrinkage and selection operator models\" paragraph 2", "quote": "LASSO regression analysis was performed using the Bioinforcloud application RunLASSO, which was developed based on the glmnet software package (Friedman et al., 2010) and extracted the expression profiles of MG dysregulated genes and co-expression module functional genes with diagnostic efficacy to construct the LASSO model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37051601", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The NAPB, C5orf25, and ERICH1 LASSO diagnostic model demonstrated excellent diagnostic efficacy for myasthenia gravis, with an AUC of 0.981.", "kind": "quantitative", "target": {"value": 0.981, "unit": "auc", "tolerance": null, "source": "ROC curve analysis showed that the MG dysregulated gene co-expression module-based model showed excellent diagnostic efficacy for MG (AUC = 0.981, Figure 4D), and some of these genes, such as NAPB, showed significantly high expression in MG, while C5orf25 and ERICH1 showed significantly low expression in MG (Figure 4E)."}, "evidence": [{"asset": "source:pmid:37051601", "locator": "sections[17] \"Myasthenia gravis dysregulated gene co-expression module-based clinical model has significant diagnostic efficacy\" paragraph 2", "quote": "ROC curve analysis showed that the MG dysregulated gene co-expression module-based model showed excellent diagnostic efficacy for MG (AUC = 0.981, Figure 4D), and some of these genes, such as NAPB, showed significantly high expression in MG, while C5orf25 and ERICH1 showed significantly low expression in MG (Figure 4E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The NAPB, C5orf25, and ERICH1 LASSO diagnostic model demonstrated excellent diagnostic efficacy for myasthenia gravis, with an AUC of 0.981.", "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:37051601"], "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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