Parkinson’s Disease as a Risk Factor for Prostate Adenocarcinoma: A Molecular Point of View

Abstract

Introduction: Cancer and neurodegeneration are two major leading causes of morbidity and death worldwide. Neurodegeneration results in excessive neuronal cell death, and cancer emerges from increased proliferation and resistance to cell death. Although most epidemiological studies support an inverse association between the risk for the development of neurodegenerative diseases and cancer, increasing evidence points to a positive correlation between specific types of cancer, like prostate adenocarcinoma (PRAD), and neurodegenerative diseases, like Parkinson’s disease (PD). Methods: PD and PRAD differential genes were screened through the GEO database, and the differential genes were analyzed using David, String, GEPIA, Kaplan-Meier plotter, TIMER2.0, proteinatlas, cBioPortal, and CTD databases to elucidate the biological function and molecular mechanism of PD and PRAD-related genes. Results: Studies have shown that the hub gene and differentially expressed genes (DEGs) in PD were differentially expressed in PRAD, including CDC20, HSPA4L, ROBO1, DMKN, IFI27L2, LUZP2, PTN, PTGDS. In PRAD, the high expression of HSPA4L, ROBO1, DMKN, IFI27L2, PTN, and PTGDS genes was associated with longer survival, while the patients with low expression of CDC20 and LUZP2 genes had longer survival. The mRNA of CDC20 and LUZP2 were highly expressed, while the mRNAs of HSPA4L, ROBO1, DMKN, IFI27L2, and PTGDS were low expressed. Gene methylation did not affect the survival of patients. The high expression of miR-142, miR-186, miR-30a, miR-497, miR-590, miR-28, and miR-576 in microRNA (miRNA) might potentially be used as biomarkers for the progression of PD and PRAD and for the early diagnosis of PD and PRAD in the populations. The genes in this study were highly associated with B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Somatic mutation mainly focused on missense mutation. Therapeutic drugs included acetaminophen and valproic acid (VPA). Conclusion: Bioinformatics was used to identify potential targets and novel molecular mechanisms that may serve as clinical markers for the diagnosis and treatment of PD and PRAD.