Identification of Potential Key Genes for the Comorbidity of Myasthenia Gravis With Thymoma by Integrated Bioinformatics Analysis and Machine Learning

Abstract

Background: Thymoma is a key risk factor for myasthenia gravis (MG). The purpose of our study was to investigate the potential key genes responsible for MG patients with thymoma.

Methods: We obtained MG and thymoma dataset from GEO database. Differentially expressed genes (DEGs) were determined and functional enrichment analyses were conducted by R packages. Weighted gene co-expression network analysis (WGCNA) was used to screen out the crucial module genes related to thymoma. Candidate genes were obtained by integrating DEGs of MG and module genes. Subsequently, we identified several candidate key genes by machine learning for diagnosing MG patients with thymoma. The nomogram and receiver operating characteristics (ROC) curves were applied to assess the diagnostic value of candidate key genes. Finally, we investigated the infiltration of immunocytes and analyzed the relationship among key genes and immune cells.

Results: We obtained 337 DEGs in MG dataset and 2150 DEGs in thymoma dataset. Biological function analyses indicated that DEGs of MG and thymoma were enriched in many common pathways. Black module (containing 207 genes) analyzed by WGCNA was considered as the most correlated with thymoma. Then, 12 candidate genes were identified by intersecting with MG DEGs and thymoma module genes as potential causes of thymoma-associated MG pathogenesis. Furthermore, five candidate key genes (JAM3, MS4A4A, MS4A6A, EGR1, and FOS) were screened out through integrating least absolute shrinkage and selection operator (LASSO) regression and Random forest (RF). The nomogram and ROC curves (area under the curve from 0.833 to 0.929) suggested all five candidate key genes had high diagnostic values. Finally, we found that five key genes and immune cell infiltrations presented varying degrees of correlation.

Conclusions: Our study identified five key potential pathogenic genes that predisposed thymoma to the development of MG, which provided potential diagnostic biomarkers and promising therapeutic targets for MG patients with thymoma.

Keywords: Myasthenia gravis; WGCNA; immune infiltration; machine learning; thymoma.

Figure 1.

Identification of the DEGs. (A) Volcano plot for MG. DEGs between MG patients with ectopic germinal centers in the thymus and without ectopic germinal centers. The red dots represented upregulated DEGs and the blue dots represented downregulated DEGs. (B) Heatmap for MG. (C) Volcano plot for thymoma (P-value < .05 and|log₂FC| > 0.25). (D) Heatmap for thymoma. DEGs indicate differentially expressed genes; MG, myasthenia gravis.

Figure 2.

Overlapping enrichment analysis of GO and KEGG pathways of DEGs in MG and thymoma. (A) The common GO enrichment analyses of DEGs in MG and thymoma. (B) The common KEGG pathway analysis of DEGs in MG and thymoma. DEGs indicate differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MG, myasthenia gravis.

Figure 3.

WGCNA result overview. (A) Sample clustering. Delete one sample outlier. (B) Soft threshold screening plot. The soft power of β = 7 was selected as the soft threshold for subsequent analyses. (C) Hierarchical clustering tree. The upper part of this figure represents the clustering of genes. The lower part represents the gene modules, which make up 18 modules. Gray represents genes that have not been classified into modules. (D) Heatmap of the correlation between the module eigengenes and clinical traits of MG. Each row represents a different gene module, and each column is a representative trait. C1 represents normal tissue and C2 represents thymoma. The value in the box represents the correlation and the P-value. Pink represents positive correlation, and green represents negative correlation. DEGs indicate differentially expressed genes; GO, Gene Ontology; MG indicates myasthenia gravis; WGCNA, weighted gene co-expression network analysis.

Figure 4.

Gene–gene interaction network and Enrichment Analysis of GO and KEGG Pathways of gene intersection. (A) Venn diagram showing the overlapping genes of the black module genes (most associated with thymoma) in WGCNA with DEGs in MG. (B) The network of 12 genes and their co-expression genes were constructed and analyzed by GeneMANIA. (C) The GO enrichment analyses of the candidate genes. (D) KEGG pathway analysis of the candidate genes. KEGG, Kyoto Encyclopedia of Genes; MG, myasthenia gravis; WGCNA, weighted gene co-expression network analysis.

Figure 5.

Machine learning in screening candidate diagnostic biomarkers for thymoma-MG. (A and B) LASSO model. The number of genes (n = 7) corresponding to the lowest point of the curve is the most suitable for thymoma-MG diagnosis. (C) Random forests rank genes based on the importance of precision. (D) Venn diagram shows that five candidate diagnostic genes are identified via the above two algorithms. LASSO indicates least absolute shrinkage and selection operator; MG, myasthenia gravis.

Figure 6.

Nomogram construction and the diagnostic value evaluation. (A) The visible nomogram for diagnosing MG with thymoma. (B to F) The ROC curve of each candidate gene (JAM3, FOS, MS4A4A, MS4A6A, and EGR1) shows the significant thymoma–MG diagnostic value. AUC indicates area under the curve; EGR1, early growth response 1; FOS, Proto-Oncogene C-Fos; JAM3, junctional adhesion molecule 3; MG, myasthenia gravis; MS4A4A, membrane-spanning 4 Domains subfamily A member 4A; MS4A6A, membrane-spanning 4 Domains subfamily A member 6A.

Figure 7.

Immune infiltration analysis in MG. (A) Bar plot for the relative abundance of 22 immune cell types in each sample. (B) Infiltration differences of immune cells in C1 and C2. C1 represents MG patients with ectopic germinal centers and C2 represents MG patients without ectopic germinal centers. MG indicates myasthenia gravis.

Figure 8.

Correlation between five key genes and specific immune cells in MG. (A) The correlation between EGR1 and memory B cells. (B) The correlation between FOS and memory B cells. (C) The correlation between MS4A6A and naive B cells. (D) The correlation between MS4A6A and M1 macrophages. (E) The correlation between MS4A4A and memory CD4T cells. EGR1 indicates early growth response 1; FOS, Proto-Oncogene C-Fos; MG, myasthenia gravis; MS4A4A, membrane-spanning 4 Domains subfamily A member 4A; MS4A6A, membrane-spanning 4 Domains subfamily A member 6A.