Differential gene expression (DGE) analysis is one of the most used techniques for RNA-seq data analysis, and it is applied in various medical and biological contexts, including biomarkers for diagnosis and prognosis and evaluation of the effectiveness of specific treatments. The conduction of a DGE analysis typically involves navigating a complex, multi-step pipeline, which usually requires proficiency in programming languages like R. This presents a barrier to researchers like biologists and clinicians, who may have limited or no coding skills, and adds additional overhead even for experienced bioinformaticians. To overcome these challenges, we propose a preliminary visual analytics prototype that simplifies DGE analysis, enabling users to perform the analyses without coding expertise.