Prostate cancer continues to pose a global health challenge as one of the most prevalent malignancies. Mutations of the Forkhead box A1 (FOXA1) gene have been linked to unique oncogenic features in prostate cancer. In this study, we aimed to unravel the intricate molecular characteristics of FOXA1 mutant prostate cancer through comprehensive in silico analysis of transcriptomic data from The Cancer Genome Atlas (TCGA). A comparison between FOXA1 mutant and control groups unearthed 1525 differentially expressed genes (DEGs), which map to eight intrinsic and six extrinsic signaling pathways. Interestingly, the majority of intrinsic pathways, but not extrinsic pathways, were validated using RNA-seq data of 22Rv1 cells from the GEO123619 dataset, suggesting complex biology in the tumor microenvironment. As a result of our in silico research, we identified novel therapeutic targets and potential drug candidates for FOXA1 mutant prostate cancer. KDM1A, MAOA, PDGFB, and HSP90AB1 emerged as druggable candidate targets, as we found that they have approved drugs throughout the drug database CADDIE. Notably, as most of the approved drugs targeting MAOA and KDM1A were monoamine inhibitors used for mental illness or diabetes, we suggest they have a potential to cure FOXA1 mutant primary prostate cancer without lethal side effects.

ETS-related gene (ERG) fusion affects prostate cancer depending on the degree of expression of ERG. Solute Carrier Family 45 Member 3 (SLC45A3) is the second-most common 5′ partner gene of ERG rearrangement. However, the molecular pathological features of SLC45A3:ERG (S:E) fusion and therapeutic methods have not been studied at all. S:E fusion-positive cancers (n = 10) were selected from the Tumor Fusion Gene Data Portal website. Fusion-negative cancers (n = 50) were selected by sorting ERG expression level in descending order and selecting the bottom to 50th sample. Totally, 1325 ERG correlated genes were identified by a Pearson correlation test using over 0.3 of absolute correlation coefficiency (|R| > 0.3). Pathway analysis was performed using over-representation analysis of correlated genes, and seven cancer-related pathways (focal adhesion kinase (FAK)/PI3K-Akt, JAK-STAT, Notch, receptor tyrosine kinase/PDGF, TGF-β, VEGFA, and Wnt signaling) were identified. In particular, focal adhesion kinase (FAK)/PI3K-Akt signaling and JAK-STAT signaling were significantly enriched in S:E fusion-positive prostate cancer. We further identified therapeutic targets and candidate drugs for S:E fusion-positive prostate cancer using gene–drug network analysis. Interestingly, PDGFRA and PDGFRB were the most frequently predicted therapeutic targets, and imatinib targeted both genes. In this study, we provide extensive information on cellular signaling pathways involved in S:E fusion-positive prostate cancer and also suggest therapeutic methods.