Objectives: Identification of the most potential druggable targets and drugs for treating pheochromocytoma by understanding the molecular mechanisms in the progression of pheochromocytoma and discovering additional biomarkers for early diagnosis. Methods: The differential gene expression in microarray data was analyzed using CRAN packages such as Geoquery, Affy, Limma, Tidyverse, GOchord, etc., and functional and pathway enrichment was performed through the David database, Further PPI network is constructed by retrieving the Protein partners were retrieved from the STRING database. The top 3 ranked gene products from cytohubba were docked with the ligand molecules, which are all in phase III trials. The complex with the highest binding affinity was simulated using molecular dynamics simulations for 100ns, PCA and FEL analysis were performed to determine the stability of the complex at each time interval additionally miRNA and TRFs were predicted for the prominent genes. Results: As a result of functional enrichment, most of the 97 differentially expressed genes (DEGs) are involved in cancer-causing pathways. Pheochromocytomas were associated with CDC42, VEGFA, PIK3R1, ITGAV, and LAMB-1, among prominent genes among the ten hub genes. Simulated results verified the effectiveness of targeting VEGFA with Lenvatinib. Conclusions: We found that differentially expressed genes in the interactome and hub genes with high correlations could serve as biomarkers, along with their miRNAs and TRFs. By targeting VEGFA as a drug target, Lenvatinib displays a high affinity for VEGFA, as it interacts with other genes through homodimerization, activation, phosphorylation, and dephosphorylation. It also appears to interact with the VEGFA protein via hydrogen bonds. In conclusion, this study suggests that Lenvatinib might be a promising anticancer agent that could treat pheochromocytoma, and VEGF-A, CDC42, PIK3R1, as well as several predicted miRNAs, such as miR-224 and miR-221, might be biomarkers for the early diagnosis.
