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

• Published in: Frontiers in genetics Vol. 14; p. 1106359
• Main Authors: Zhang, Demin, Luo, Liqin, Lu, Feng, Li, Bo, Lai, Xiaoyun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 27.03.2023
• Subjects: biomarkers; Genetics; infiltrated immune cells; LASSO; myasthenia gravis; WGCNA; myasthenia gravis; infiltrated immune cells; WGCNA; biomarkers; LASSO
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2023.1106359

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.