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 ca

SUBMITTER: Liu H 

PROVIDER: S-EPMC11437577 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature