Identification of a diagnostic gene signature for the differentiation of the organ of origin for adeoncarcinomas at the esophagogastric junction (AEG).

在目前的第7版UICC TNM分类中，食管胃交界处腺癌（AEG）被视为一个单一实体，导致统一的治疗算法。起源器官是远端食管或近端胃。流行病学数据指出，由于影响这种恶性疾病预后的不同生物风险因素，每个器官的致癌机制不同。到目前为止，起源器官的分类仅通过某些形态学特征（如男性肿瘤块的定位）或替代物（如Barretts化生的存在）进行。一个有效的标志物面板，允许食管或胃起源的肿瘤的分化，不仅有利于流行病学研究评估AEG患者的个人风险状况，但可以进一步提供个性化的治疗algorithm.The未来的方法的背景介绍基于组织的阵列技术，使大量的不同基因的差异表达的同时分析。为了评估和进一步评价这些技术产生的数据计算，生物统计学方法是必需的。在共表达网络中的基因-基因-相互作用的系统评估使得能够分层成功能模块，所述功能模块通过功能和形态标准分类成明确定义的术语（基因本体术语）。因此，不仅可以鉴定通过起源器官区分癌的特异性基因标记，而且可以通过功能标准选择这些基因，以鉴定作为治疗化合物的推定靶标的候选基因。对于主要生物统计学分析，不强制生成从头阵列数据。人们也可以访问公开的，已经发布的数组数据集。拟议的项目包括三个主要部分。最初，公开可用的数据集进行评估的生物统计学方法，以证明差异基因表达的食管和胃起源的腺癌。然后，这些数据用于生成基因签名，其允许通过基于组织的阵列评估通过起源位点区分AEG。第二步包括在一小部分患者中对该基因签名进行原理验证，然后在一个大型的独立AEG患者队列中对该签名进行主要验证。第三步，进一步表征所包含的靶基因的分子功能，以阐明其作为治疗靶点的假定作用。在体外环境中，将评估每个基因对相关信号传导途径的调节作用。这主要包括负责细胞增殖和分化的调节途径。

In the current 7th edition of the TNM classification of the UICC adenocarcinomas at the esophagogastric junction (AEG) are regarded as one single entity resulting in uniform treatment algorithms. Organ of origin is either the distal esophagus or the proximal stomach. Epidemiological data point towards distinct mechanisms of carcinogenesis for each organ due to different biological risk factors that influence the prognosis of this malignant disease. Up to today, classification of the organ of origin is only done by certain morphological features like localisation of the man tumor mass or by surrogates like presence of Barretts metaplasia. A valid marker panel that allows the differentiation of tumors of esophageal or gastric origin, would not only facilitate epidemiological studies assessing the individual risk profile for AEG patients but could furthermore deliver the background for future approaches of individualized therapeutic algorithms.The introduction of tissue-based array-techniques enables the simultaneous analysis of the differential expression of a high number of different genes. For assessment and further evaluation of the by these techniques generated data computational, biostatistical methods a required. The systematic assessment of gene-gene-interactions in co-expression networks enabled the stratification into functional modules which are classified by functional and morphological criteria into clearly defined terms (gene ontology terms). Thus, it is not only possible to identify a specific gene signature that differentiates carcinomas by there organ of origin, but also to select these genes by functional criteria in order to identify candidate genes that are putative targets for therapeutic compounds. For the primary biostatistical analysis, it is not mandatory to generate de novo array data. One can also take access publicly available, already published array data sets.The proposed project comprises three main parts. Initially, publicly available data-sets are evaluated by biostatistical methods to demonstrate differential gene expression in adenocarcinomas of esophageal and gastric origin. Then, these data are used to generate a gene-signature that allows the differentiation of AEG by the site of origin via tissue-based array assessment. The second step includes the proof-of-principle confirmation of this gene-signature in a small subset of patients, before the main validation of the signature is performed in a large, independent cohort of patients with AEG.In a third step, the included target genes are further characterized concerning there molecular function in order to elucidate there putative role as therapeutic target. In an in vitro setting the modulatory effect of each gene on relevant signalling pathways will be assessed. This includes mainly regulatory pathways, responsible for cell proliferation and differentiation.