Gene-Gene Interactions and Their Functional Roles in Prostate Cancer Aggressiveness

项目摘要/摘要 前列腺癌是最常见的癌症，具有很强的临床异质性。 美国男性癌症相关死亡的第二大原因。目前还不清楚为什么有些人 前列腺癌比其他肿瘤更具侵袭性。现有的临床特征(如前列腺癌 特异性抗原(PSA)、临床分期和Gleason评分)不足以区分高分化型 和低风险前列腺癌患者。研究表明，大约20%的低风险患者 前列腺癌患者因保守治疗而死亡。因此，迫切需要 寻找更多的生物标志物以提高前列腺癌的预测准确性 咄咄逼人。目前的大多数研究都集中在评估个体的遗传变异上， 这可能不足以解释疾病因果关系的复杂性。这样做的目的是 研究是确定四条候选途径内的基因-基因相互作用(血管生成， 线粒体、miRNA和雄激素代谢)与前列腺癌相关 攻击性及其对基因表达的影响。遗传变异(两者都是个体 效应和相互作用)与前列腺癌侵袭性相关 使用来自大规模前列腺癌联盟的现有基因数据，收集了 大约22,000名前列腺癌患者。基因变异和遗传变异之间的联系 基因表达将使用公共领域遗传数据进行识别，并将使用 对1065名前列腺癌患者的队列数据集。用基因评估遗传变异 表达水平有助于识别下游基因，从而指导进一步研究，并可能 导致新的治疗靶点的发现。我们的研究结果可以提供有价值的 前列腺癌发病机制及基因分型的研究进展 预测前列腺癌侵袭性的组合。至于长期影响， 研究结果可能应用于开发预测前列腺癌的有效筛查工具 咄咄逼人。

Project Summary/Abstract 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.