Plasma Microarray Analysis and Ovarian Cancer Biomarkers

描述（由申请人提供）：本R03提案的主要目的是使用循环血浆RNA中可检测的基因表达谱鉴定用于卵巢癌早期检测的潜在生物标志物。卵巢癌的死亡率很高，主要是因为没有有效的早期发现方法。由于卵巢癌在早期发现时是可以治愈的，因此开发可靠的早期检测血液检测将极大地影响生存率。最近，循环核酸已被描述为一类新兴的分子肿瘤标志物，具有在癌症筛查中的潜在应用。我们有初步的数据表明，循环血浆RNA的微阵列分析可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，并进行全基因组表达分析，以鉴定将卵巢癌妇女与健康对照区分开的RNA标记物。因此，我们计划扩大我们的初步调查结果，使用样本收集的一部分，人口为基础的病例对照研究在坦帕湾地区。从60名患有浸润性上皮性卵巢癌的妇女和40名健康对照妇女获得的血浆RNA将进行扩增和微阵列分析，所述妇女基于年龄、种族、绝经状态和居住县与病例匹配。将使用贝叶斯回归分析比较病例和对照之间的基因表达模式，并鉴定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先前使用微阵列分析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生物标志物”，使用定量实时PCR进行验证。我们预计，拟议研究的结果将为未来R01资助的对早期卵巢癌检测中具有临床实用性的RNA生物标志物进行更全面的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.