Prostate cancer with substantial clinical heterogeneity is the most common cancer and the second leading cause of cancer-related death in American men. It remains unclear why some prostate tumors are more aggressive than others. Existing clinical features (such as prostate specific antigen (PSA), clinical stage and Gleason score) are not sufficient for classifying high- and low-risk prostate cancer patients. It has been shown that approximately 20% of low-risk prostate cancer patients died due to conservative treatment. Thus, there is an urgent need for identifying additional biomarkers in order to improve prediction accuracy of prostate cancer aggressiveness. The majority of current studies focus on evaluating individual genetic variants, which may not be sufficient to explain the complexity of disease causality. The objective of this study is to identify gene-gene interactions within the four candidate pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism) associated with prostate cancer aggressiveness and their impact on gene expression. The genetic variants (both individual effects and interactions) associated with prostate cancer aggressiveness will be performed using the existing genetic data from the large scale prostate cancer consortium, a collection of approximately 22,000 prostate cancer patients. The associations between genetic variants and gene expressions will be identified using public domain genetic data and will be validated using a cohort data set with 1065 prostate cancer patients. Evaluating genetic variants with gene expression levels helps to identify downstream genes which can guide further study and may lead to discovery of novel therapeutic targets. Our study findings can provide valuable information toward understanding pathogenesis of prostate cancer and identifying genotype combinations for predicting prostate cancer aggressiveness. As for the long-term impact, the study results may be applied in developing effective screening tools to predict prostate cancer aggressiveness.
For prostate cancer patients, physicians often have difficulty at the time of a prostate cancer diagnosis distinguishing between patients who will develop indolent tumors and those who will develop aggressive tumors. This study aims to identify genetic markers (genetic variants and gene expressions) involved with multiple genes associated with prostate cancer aggressiveness. Our study findings can provide valuable information toward understanding pathogenesis of prostate cancer and identifying genotype combinations for predicting prostate cancer aggressiveness.
| Lin, Hui-Yi; Chen, Dung-Tsa; Huang, Po-Yu et al. (2017) SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns. Bioinformatics 33:822-833 |
| Lin, Hui-Yi; Cheng, Chia-Ho; Chen, Dung-Tsa et al. (2016) Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer. Transl Cancer Res 5:S951-S963 |
