Plasma Microarray Analysis and Ovarian Cancer Biomarkers

描述(由申请人提供)：R03提案的主要目的是利用循环血浆RNA中可检测到的基因表达谱识别潜在的生物标记物，用于卵巢癌的早期检测。卵巢癌的死亡率很高，主要是因为目前还没有被证明有效的早期检测方法。由于卵巢癌在早期发现时是可以治愈的，开发一种可靠的早期检测血液测试将极大地影响患者的存活率。近年来，循环核酸被认为是一类在肿瘤筛查中具有潜在应用前景的分子肿瘤标志物。我们有初步数据表明，循环血浆RNA的微阵列分析可以用于识别可能预测卵巢癌存在的RNA生物标志物。我们的总体假设是，血浆中循环的RNA可以被检测、扩增，并接受全基因组表达分析，以确定区分卵巢癌妇女和健康对照的RNA标记。因此，我们计划利用在坦帕湾地区以人群为基础的病例对照研究中收集的样本来扩大我们的初步发现。从60名浸润性上皮性卵巢癌患者和40名健康对照女性身上提取的血浆RNA将进行扩增和微阵列分析，这些患者的年龄、种族、绝经状况和居住地与患者匹配。将使用贝叶斯回归分析比较病例和对照组之间的基因表达模式，并识别预测卵巢癌存在的基因。通过将这些血浆RNA基因表达模式与我们之前利用微阵列分析120例原发性卵巢癌的数据进行比较，我们将选择一组候选RNA生物标记物，并使用实时定量PCR进行验证。我们预计，拟议研究的结果将为未来由R01资助的更全面的RNA生物标记物在卵巢癌早期检测中的临床实用分析提供动力和理由。

DESCRIPTION (provided by applicant): The primary aim of this R03 proposal is to identify potential biomarkers for the early detection of ovarian cancer using gene expression profiles detectable in circulating plasma RNA. Ovarian cancer has a high mortality, mainly because there is no proven effective method for early detection. Since ovarian cancer is curable when identified early, development of a reliable blood test for early detection would dramatically impact survival. Recently, circulating nucleic acids have been described as an emerging class of molecular tumor markers with potential application in cancer screening. We have preliminary data to suggest that microarray analysis of circulating plasma RNA can be utilized to identify RNA-biomarkers that may be predictive of the presence of ovarian cancer. Our overall hypothesis is that RNA circulating in plasma can be detected, amplified, and subject to genome-wide expression analysis to identify RNA markers that differentiate women with ovarian cancer from healthy controls. We therefore plan to extend our preliminary findings using samples collected as part of a population-based case-control study in the Tampa Bay area. Plasma RNA obtained from 60 women with invasive epithelial ovarian cancer and 40 healthy control women matched to cases based on age, race, menopausal status and county of residence, will be subject to amplification and microarray analysis. Gene expression patterns will be compared between cases and controls using Bayesian regression analysis, and genes predictive of the presence of ovarian cancer will be identified. By comparing these plasma RNA gene expression patterns with data we previously developed using microarray analysis of 120 primary ovarian cancers, we will select a panel of "candidate RNA biomarkers" to be validated using quantitative real-time PCR. We anticipate that findings from the proposed study will provide impetus and justification for a more comprehensive future R01-funded analysis of RNA biomarkers with clinical utility in early ovarian cancer detection.