Unveiling the Molecular Landscape of FOXA1 Mutant Prostate Cancer: Insights and Prospects for Targeted Therapeutic Strategies.
Kyung Won HwangJae Won YunHong Sook KimPublished in: International journal of molecular sciences (2023)
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.
Keyphrases
- prostate cancer
- radical prostatectomy
- rna seq
- single cell
- mental illness
- transcription factor
- wild type
- type diabetes
- global health
- genome wide
- signaling pathway
- mycobacterium tuberculosis
- mental health
- public health
- cardiovascular disease
- risk assessment
- gene expression
- squamous cell carcinoma
- molecular docking
- big data
- papillary thyroid
- metabolic syndrome
- cancer therapy
- oxidative stress
- adipose tissue
- copy number
- adverse drug
- epithelial mesenchymal transition
- single molecule
- machine learning
- data analysis
- squamous cell
- bioinformatics analysis