Integrative bioinformatics and drug repurposing for metastatic prostate cancer: identifying novel therapeutic targets by transcriptional profiling and molecular Modeling.

Abstract

Metastasis is one of the leading factors of cancer-related deaths worldwide. New potential targets and treatment strategies are needed to extend survival and enhance the quality of life for these patients. We performed an in-depth bioinformatics analysis to identify potential genes and associated potential therapeutic compounds for metastasis of prostate adenocarcinoma. The differentially expressed genes (DEGs) were first identified using four datasets (GSE8511), (GSE3325), (GSE27616) and (GSE6919) present in the Gene Expression Omnibus (GEO) database and analyzed using the GEO2R. WGCNA was performed to find a significant gene cluster. Network analysis was performed using MCODE and Cytohubba plugins of Cytoscape to select hub genes. Moreover, expression validation of key genes was carried out using the TCGA dataset. Functional annotation and pathway enrichment analyses were conducted for validation, while survival analysis was applied to assess potential therapeutic effects. DEGs retrieved from the GEO were submitted to the Connectivity Map database to identify potentially related compounds. Molecular docking, ADMET analysis and drug-likeness properties, MD simulations and MM-GBSA analysis were performed to screen for the best potential drugs. We identified three compounds-Prunetin, Ofloxacin, and ALW-II-49-7 that may help extend disease-free survival in patients with tumor metastasis. Additionally, ACTA2, MYLK, and CNN1 were recognized as potential therapeutic targets for these compounds. These drugs' potential effectiveness and binding efficiency were screened using induced fit molecular docking followed by 100 ns MD-based Simulations and MM-GBSA analysis. However, further in vitro and in vivo studies are needed to confirm these findings. Insight box This study integrates microarray gene expression profiling with bioinformatics tools to identify differentially expressed genes (DEGs) and co-expression networks using WGCNA. Network analysis in Cytoscape was used to screen hub genes, and the Connectivity Map (cMAP) database was searched for potential candidate drugs. Binding efficiency of repurposed drugs was evaluated using molecular docking, molecular dynamics (MD) simulations, and MM-GBSA analysis. Our findings provide the potential therapeutic drugs and targets of prostate adenocarcinoma metastasis with possibilities for follow-up in vitro and in vivo validation.