Identification and validation of H. pylori-associated gastric cancer biomarkers in Chinese and German populations

胃癌(GC)是全球癌症相关死亡的第三大原因，在亚洲发病率尤其高。胃肿瘤的发生是一个多因素的过程，其中幽门螺杆菌感染是主要的危险因素，个体风险受遗传和表观遗传因素的调节。慢性感染激活了许多炎症信号通路，导致DNA损伤，促进了胃粘膜病变的发生和发展。由于缺乏有效的早期诊断生物标志物以及有限的治疗策略而导致的延迟发现是GC患者预后较低的主要原因。不同的研究旨在识别新的生物标志物，以提高非侵入性手段的早期检测，然而，到目前为止，还没有一个单一的标志物具有足够的预测能力来识别早期阶段。因此，识别生物标记物是解决这一高度医学需求的一个重要方面。重要的是，通过非侵入性方法检测生物标志物对一般临床实践具有重要意义，特别是在发病率高且广大人群难以获得胃镜检查的国家。这种情况在许多亚洲国家都存在。因此，我们的目标是建立一种基于外周血中可检测到的生物标记物的组合来筛选高危人群的与幽门螺杆菌感染相关的GC的筛查算法。这种预测模型最终可能成为一种强大的工具，对GC的发病率和死亡率产生重大影响。

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide, with incidence being especially high in Asia. The development of gastric tumors is a multifactorial process in which infection with Helicobacter pylori is the main risk factor, while the individual risk is modulated by genetic and epigenetic factors. The chronic infection activates a number of inflammatory signaling pathways and induces DNA damage, contributing to the development and progression of precursor gastric lesions to GC. Late detection due to the lack of efficient biomarkers for early diagnosis as well as limited therapeutic strategies are the major reasons for the low prognosis observed in GC patients. Different studies have aimed at identifying novel biomarkers to improve early detection by non-invasive means, however, no single marker with sufficient predictive power to identify early stages has been demonstrated so far. Thus, the identification of biomarkers is an important aspect to address this high medical need. Importantly, the detection of biomarkers by non-invasive methods is of major significance for general clinical practice, especially in countries where prevalence is high and gastroscopic screening is not readily available for the broad population. This situation is found in many Asian countries. Therefore, we aim at establishing a screening algorithm for GC associated with H. pylori infection based on a combination of biomarkers detectable in peripheral blood which can be utilized to screen populations at high risk. This predictive model might eventually represent a powerful tool with a major impact on GC morbidity and mortality.