NSAIDS and Other Medications in Prostate Cancer Etiology

前列腺癌是男性中最常见的恶性肿瘤，但其病因仍不清楚。 致癌过程的一个中心主题是不受控制的细胞生长，这是细胞增殖和细胞死亡之间平衡的干扰。 刺激细胞增殖的因素增加了随机遗传错误积累和恶性表型出现的机会。 阻断凋亡的因素通常会将DNA损伤的细胞从周期中清除，可能会促进恶性细胞的持续生长。 有令人信服的证据表明，某些药物可能通过改变前列腺细胞生长或死亡率的共同生物学途径作为前列腺癌的风险或保护因素。 为了研究这些常用药物与前列腺癌之间的关系，我们提出了一项基于人群的病例对照研究，包括1000例病例和1000例对照，年龄在40-74岁之间，将检验以下假设：1。使用非甾体抗炎药与前列腺癌风险降低相关; 2.使用他汀类药物与前列腺癌风险降低有关; 3.使用钙通道阻滞剂与前列腺癌风险增加相关; 4.使用组胺H2受体拮抗剂与前列腺癌风险增加相关。我们还将评估参与药物代谢的CYP 2C 9基因的遗传多态性。 将使用Logistic回归模型估计与使用每种药物相关的前列腺癌相对风险，调整潜在的混杂因素。 肿瘤分期和分级将用于评估相关性是否因疾病侵袭性而异。 鉴于这些药物的暴露频率不断增加，这项研究的信息将是及时的，也是迫切需要的。 这项研究的结果可能为这种复杂疾病的发病机制提供独特的见解，并为前列腺癌的预防提供创新策略。

Prostate cancer is the most common malignancy in men, yet its etiology remains obscure. A central theme of the carcinogenic process is uncontrolled cell growth, which is a disturbance in the balance between cell proliferation and cell death. Factors that stimulate cell proliferation enhance the opportunity for accumulation of random genetic errors and emergence of a malignant phenotype. Factors that block apoptosis, which would normally remove DNA-damaged cells from the cycle, may promote continued growth of malignant cells. There is compelling evidence that certain medications may act as risk or protective factors for prostate cancer through a common biological pathway involving altered rates of prostatic cell growth or death. To study the relationships between these frequently used medications and prostate cancer, we propose a population-based case-control study of 1000 cases and 1000 controls, aged 40-74 years, that will test the following hypotheses: 1. Use of nonsteroidal anti-inflammatory drugs is associated with a reduced risk of prostate cancer; 2. Use of statins is associated with a reduced risk of prostate cancer; 3. Use of calcium-channel blockers is associated with an increased risk of prostate cancer; and 4. Use of histamine H2-receptor antagonists is associated with an increased risk of prostate cancer. We also will assess genetic polymorphism in the CYP2C9 gene, which is involved in drug metabolism. Logistic regression models will be used to estimate relative risks of prostate cancer associated with use of each type of medication, adjusting for potential confounding factors. Tumor stage and grade will be used to assess whether associations vary by disease aggressiveness. Given the rising frequency of exposure to some of these medications, the information from this study will be timely and is urgently needed. Results of the study may provide unique insights on the pathogenesis of this complex disease and lead to innovative strategies for prostate cancer prevention.