Identification of Differentially Expressed Genes and Drug Targets in Prostate Cancer Using Next-Generation Sequencing

Prostate cancer, a leading malignancy with significant impact on men’s health, was the focus of this study, which aimed to identify candidate genes through differential gene expression analysis using Galaxy, an open-source platform for analyzing next-generation sequencing data. RNA-Seq analysis was performed on several datasets from the GEO database, comparing nine tumor patient datasets with eight non-tumor datasets. This analysis revealed ten upregulated and ten downregulated genes with log2FC counts > 2.5 and p-values < 0.05. To further investigate these differentially expressed genes (DEGs), WebGestalt was used for comprehensive in silico analysis, visualizing enrichment via volcano plots. Additionally, protein-protein interaction (PPI) networks were constructed using STRING, identifying three gene modules and ten hub genes through Cytoscape cluster analysis. Molecular docking studies were then conducted on these hub genes using PyRx software, with protein structures retrieved from the Protein Data Bank (PDB). This included the addition of polar hydrogen atoms, the assignment of partial charges, and the removal of water molecules to prepare for efficient molecular docking. This research enhances our understanding of gene interactions and protein-phytochemicals binding mechanisms, thereby contributing to therapeutic advancements in the biopharmaceutical sector.