Identification of risk genes for gastric cancer

胃腺癌是德国第五大常见癌症。在大多数情况下，病因是多因素的，除了环境影响外，遗传因素在疾病发展中也起作用。遗传风险因素可以通过使用大型病例对照队列的全基因组关联研究（GWAS）来确定。因此，确定的风险基因的数量显然取决于GWAS样本的大小。风险基因可以进一步用于系统生物学研究，以确定GWAS风险基因富集的相互作用蛋白质的细胞网络。申请人已经建立了一个名为星星（http：//star-project.md/）的国际研究网络，目的是破译GC的遗传和细胞生物学原因。因此，我们建立了一个由欧洲血统的胃癌患者组成的生物样本库，该项目的目的是确定胃癌的遗传风险变异。[A]分子遗传学工作：（i）使用来自星星（4，000名患者）的生物库，我们将进行第一阶段GWAS，并将鉴定欧洲人群中GC的第一个遗传风险变体。(ii)此外，将使用来自两项亚洲研究的GC GWAS数据进行跨血统荟萃分析（8，000例患者）。这将能够识别跨种族GC风险变体。(iii)此外，还将进行一项荟萃分析，纳入来自胃食管连接部腺癌样本的GWAS数据（7，000例患者）。这将导致上消化道腺癌的常见遗传风险变异的识别。(iv)GWAS数据也将与H的GWAS结果进行比较。pylori感染，这将有助于我们识别H.幽门螺杆菌感染与胃癌然后将在生物信息学水平上研究最相关的遗传风险变体和相应的基因。因此，我们将获得第一次深入了解病理生理相关的细胞网络的基础GC。利用患者和GWAS变体的表型数据，我们将最终进行基因型-表型和基因型-环境分析，这将定义基于生物学的GC亚型。[B]招募工作：同时，我们将扩大星星的生物库。除了DNA样本，我们还希望在诊断性胃镜检查或手术期间收集从胃的不同部位获得的组织样本。该生物库将用于第2阶段GWAS和细胞生物学研究，这两项研究都将是未来应用的主题。所识别的风险变体和基因将为阐明GC的病理生理学做出重要贡献。这将导致新的治疗靶点和生物标志物的诊断和预防的识别。

Gastric adenocarcinoma represents the fifth most common cancer in Germany. In most cases the etiology is multifactorial and, in addition to environmental influences, genetic factors play a role in the disease development. Genetic risk factors can be identified through genome-wide association studies (GWAS) using large case-control cohorts. Accordingly, the number of identified risk genes clearly depends on the size of GWAS samples. The risk genes can be further used for system biology studies in order to identify cellular networks of interacting proteins in which GWAS risk genes are enriched. The applicants have established an international research network called STAR (http://star-project.md/) with the purpose of deciphering the genetic and cell biological causes of GC. Therefore, a biobank has been set up with GC patients of European descent.The aim of the proposed project is the identification of genetic risk variants for GC. [A] Molecular genetic work: (i) Using the biobank from STAR (4,000 patients) we will perform a stage-1 GWAS and will identify the first genetic risk variants for GC in the European population. (ii) In addition, a trans-ancestry meta-analysis using GC GWAS data from two Asian studies will be carried out (8,000 patients). This will enable the identification of trans-ethnic GC risk variants. (iii) Furthermore, a meta-analysis incorporating GWAS data from a sample with adenocarcinoma of the gastroesophageal junction will be done (7,000 patients). This should lead to the identification of common genetic risk variants for adenocarcinomas of the upper gastrointestinal tract. (iv) The GWAS data will also be compared to GWAS results for H. pylori infection, which will help us to identify genetic risk variants for H. pylori infection and GC. The most associated genetic risk variants and corresponding genes will then be studied on the bioinformatic level. Thus, we will gain first insights into pathophysiologically relevant cellular networks underlying GC. With phenotypic data of patients and GWAS variants, we will finally perform genotype-phenotype and genotype-environmental analyses, which will define biologically-based GC subtypes. [B] Recruitment work: In parallel, we will extend the biobank of STAR. Besides DNA samples, we anticipate also to collect tissue samples obtained from different parts of the stomach during diagnostic gastroscopy or surgery. The biobank will be used for a stage-2 GWAS and cell biological studies, which both will be subject of future applications.The identified risk variants and genes will make an important contribution in the elucidation of the pathophysiology of GC. This will lead to the identification of new therapeutic targets and biomarkers for the diagnosis and prevention.