Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005–2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.