Translational Oncology (Mar 2022)
A network-based integration for understanding racial disparity in prostate cancer
Abstract
Compared to Caucasians (CAs), African Americans (AAs) have a higher rate of incidence and mortality in prostate cancer and are prone to be diagnosed at later stages. To understand this racial disparity, molecular features of different types, including gene expression, DNA methylation and other genomic alterations, have been compared between tumor samples from the two races, but led to different disparity associated genes (DAGs). In this study, we applied a network-based algorithm to integrate a comprehensive set of genomic datasets and identified 130 core DAGs. Out of these genes, 78 were not identified by any individual dataset but prioritized and selected through network propagation. We found DAGs were highly enriched in several critical prostate cancer-related signaling transduction and cell cycle pathways and were more likely to be associated with patient prognosis in prostate cancer. Furthermore, DAGs were over-represented in prostate cancer risk genes identified from previous genome wide association studies. We also found DAGs were enriched in kinase and transcription factor encoding genes. Interestingly, for many of these prioritized kinases their association with racial disparity did not manifest from the original genomic/transcriptomic data but was reflected by their differential phosphorylation levels between AA and CA prostate tumor samples. Similarly, the disparity relevance of some transcription factors was not reflected at the mRNA or protein expression level, but at the activity level as demonstrated by their differential ability in regulating target gene expression. Our integrative analysis provided new candidate targets for improving prostate cancer treatment and addressing the racial disparity problem.
