Abstract
Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the pleural membrane. It is characterized by its rarity and lethality, presenting limited treatment avenues. Vascular endothelial growth factor A (VEGF-A) is a crucial signaling protein that binds to and activates the VEGFR-2 (vascular endothelial growth factor receptor-2). This interaction initiates a signaling cascade promoting endothelial cell proliferation and migration, fostering the formation of new blood vessels and fueling tumor growth. In this study, immunogenic approaches were employed to predict potential antigenic epitopes targeting the VEGFR-2 receptor. These epitopes were utilized to construct a multi-epitope peptide, which was subsequently docked against VEGFR-2 receptors to assess binding capabilities and interactions. Three optimal epitopes from the vaccine construct were selected based on binding interactions. Peptides derived from these epitopes were then predicted and docked with VEGFR-2 receptors to analyze their binding abilities and interactions further. Moreover, three distinct datasets for MPM were selected, each representing a different condition: a normal dataset, a diseased dataset, and a dataset from treated individuals (using K-975 - Kirin). Common genes across these datasets were identified through Next-Generation Sequencing (NGS) analysis conducted on an online Galaxy server. Additionally, upregulated genes in malignant pleural mesothelioma were detected through NGS analysis, with sulf-1 identified as an upregulated gene protein. Molecular docking analysis against a phytochemical library was performed using sulf-1, leading to the selection of novel drugs acting as inhibitors against malignant pleural mesothelioma. Furthermore, the pharmacokinetic properties of selected compounds were analyzed. This study aims to screen potential inhibitors targeting VEGFA, block the VEGFR-2 receptor using immunogenic and non-toxic peptides derived from VEGFA, and identify differentially expressed genes in malignant mesothelioma through RNA sequencing for virtual screening of potential inhibitors.