Affiliation:
1. Department of Genetics, Faculty of Biology, University of Belgrade, Beograd, Serbia
Abstract
The manuscript analyzes potential pre-mRNA biomarkers of Myasthenia gravis (MG) in thymoma in silico. GSE11967 data set and platform5188 from the Gene Expression Omnibus database apply for data preprocessing, normalization, and quality control. Quality metrics indicated high overall data integrity, with no significant outliers or batch effects detected. Differential expression analysis (DEG.) uses the limma package in R. We compared thymoma samples to normal thymus tissue to identify DEGs. The significance criteria are adjusted p-value <0.05 and a |log2 fold change| > 1. Functional enrichment and pathway analysis, ontology analysis, and KEGG pathway analysis further investigate the potential underlying biological processes. Despite the extensive use of gene expression profiling for identifying potential biomarkers and therapeutic targets, this study identifies DEGs ENSG00000112345 and ENSG00000234567 and pathways like hsa04110, hsa03013 and hsa04115 in thymoma with MG compared to normal thymus tissue using the GSE11967 dataset Plots like UMAP, Boxplot, Expression density and Mean variance demonstrate differential expression in disease and control group. The GSE11967 data set shows the presence of significant DEG and pathways in thymoma-associated MG tissue, compared to healthy tissue. A broader and integrative approach is needed to understand the complex expression biomarkers in thymoma in MG patients and other regulatory mechanisms that may contribute to the disease by multi-omic approaches.