Epidemiology and biology of lncRNAs in ovarian cancer

项目总结 全基因组关联研究(GWAS)无疑是成功的。对于上皮性 卵巢癌(EOC)，我们的国际卵巢癌协会联合会(OCAC) 为发现所有已知的风险基因做出了贡献。虽然肯定存在更多的基因座，但 预期的适度效应量需要较大的样本量，以允许在接受的情况下进行区分 全基因组意义的水平。因为世界上几乎所有的病例对照研究都是 作为NCI游戏的一部分，已经在这些努力中进行合作--关于OncoArray基因分型 在这一倡议下，增加样本量在短期内不是一种选择。相反，进一步的进步 需要创新性地使用大部分现有的流行病学和分子数据集。在 拟议的研究，我们试图建立在观察到的基础上，即大多数GWAS的“命中”驻留在非 基因组的编码区和我们的初步数据支持这样的假设 EoC易感性的很大一部分个体间差异反映了单核苷酸 长非编码RNA(LncRNAs)中的SNPs。我们的项目将包括三个 阶段。首先，我们将对所有已知的与lncRNAs相关的种系遗传变异进行分类。 使用超过28,000例和36,000例患者的现有基因数据和EoC易感性 来自OCAC的对照归因于1000基因组计划密度，来自MOST的注释 全面的lncRNA数据库(GENCODE)和来自最大的RNA测序的数据 为识别特定于EoC的LncRNAs而执行的项目。然后我们将检测到相关的lncRNA 使用现有的RNA测序(RNA-SEQ)的EOC和微阵列数据加上 从整个转录组阵列中产生。LncRNA表达数量性状基因座(Lnc- 将进行eQTL)分析以识别最有希望的LncRNA SNPs和LncRNAs， 并将进行网络分析，以突出可能支撑EOC的监管网络 发展。在最后阶段，我们将在实验室中跟进最有希望的发现 探讨其功能和生物学基础。这种综合的分子流行病学方法 利用OCAC独有的资源和专业知识可能会导致 具有临床应用价值的新SNPs和LncRNAs的鉴定和鉴定 减轻平机会的负担。

PROJECT SUMMARY Genome-wide association studies (GWAS) have been unequivocally successful. For epithelial ovarian cancer (EOC), our international Ovarian Cancer Association Consortium (OCAC) has contributed to the discovery of all known risk loci. Although more loci certainly exist, the anticipated modest effect sizes require large sample sizes to permit discrimination at accepted levels of genome-wide significance. Since virtually every case-control study in the world is already collaborating on these efforts as part of NCI's GAME-On OncoArray genotyping initiative, increasing the sample size is not an option in the near term. Rather, further progress requires innovative use of largely existing epidemiologic and molecular datasets. In the proposed study, we seek to build upon the observation that most GWAS “hits” reside in non- coding regions of the genome and our preliminary data that support the hypothesis that a significant portion of inter-individual variability in EOC susceptibility reflects single nucleotide polymorphisms (SNPs) in long non-coding RNAs (lncRNAs). Our project will include three phases. First, we will catalogue all known germline genetic variants associated with lncRNAs and EOC susceptibility using existing genotype data on more than 28,000 cases and 36,000 controls from OCAC imputed to 1000 Genomes Project density, annotation from the most comprehensive lncRNA database (GENCODE), and data from the largest RNA-sequencing project performed to identify EOC-specific lncRNAs. We will then detect lncRNAs associated with EOC using existing RNA-sequencing (RNA-seq) and microarray data plus data to be generated from a whole transcriptome array. LncRNA expression quantitative trait loci (lnc- eQTL) analysis will be performed to identify the most promising lncRNA SNPs and lncRNAs, and network analyses will be conducted to highlight regulatory networks that may underlie EOC development. In the final phase, we will follow-up the most promising findings in the laboratory to explore the functional and biological basis. This integrative molecular epidemiologic approach which capitalizes on resources and expertise that are unique to OCAC may lead to the identification and characterization of novel SNPs and lncRNAs that may be of clinical utility in reducing the burden of EOC.