NSAIDS and Other Medications in Prostate Cancer Etiology

前列腺癌是男性最常见的恶性肿瘤，但其病因尚不清楚。致癌过程的一个中心主题是细胞生长失控，这是细胞增殖和细胞死亡之间平衡的失衡。刺激细胞增殖的因素增加了随机遗传错误积累和出现恶性表型的机会。阻止细胞凋亡的因素通常会将DNA受损的细胞从细胞周期中移除，但可能会促进恶性细胞的持续生长。有令人信服的证据表明，某些药物可能通过一种常见的生物学途径作为前列腺癌的风险或保护因素，涉及前列腺细胞生长或死亡的改变。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，涉及1000名病例和1000名对照，年龄在40-74岁之间，该研究将检验以下假设：1.非类固醇抗炎药的使用与前列腺癌风险的降低相关；2.他汀类药物的使用与前列腺癌风险的降低相关；3.钙通道阻滞剂的使用与前列腺癌风险的增加相关；以及4.组胺H2受体拮抗剂的使用与前列腺癌风险的增加相关。我们还将评估与药物代谢有关的CYP2C9基因的遗传多态。Logistic回归模型将被用来估计与使用每种药物相关的前列腺癌的相对风险，并对潜在的混杂因素进行调整。肿瘤分期和分级将被用来评估相关性是否因疾病侵袭性而异。鉴于接触其中一些药物的频率不断上升，这项研究的信息将是及时的，也是迫切需要的。研究结果可能为这种复杂疾病的发病机制提供独特的见解，并导致前列腺癌预防的创新策略。

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.