Quantification of the Landscape for Revealing the Underlying Mechanism of Intestinal-Type Gastric Cancer

Gastric cancer is a daunting disease with a tragic impact on global health. It is the fourth most common cancer and has become the second most frequent cause of cancer death in recent times. According to the Lauren classification, gastric cancer can be classified into two types: intestinal and diffuse. Intestinal-type gastric cancer (IGC) is more common in elderly people, and atrophic gastritis (AG) and intestinal metaplasia (IM) have been proven to be the main premalignant causes of intestinal-type gastric cancer. In turn, Helicobacter pylori infection has been identified as the most significant cause of AG and IM. In this study, we determine the mechanism of IGC progression and how H. pylori infection induces IGC. Through researching the relevant literature, we identified the key genes associated with gastric cancer and the specific genes associated with IGC. We then use hese genes to build up a gene regulatory network for IGC. Based on this gene regulatory network, we quantify the IGC landscape. Within this landscape, there are three stable states, which are classified as the normal, AG, and gastric cancer states. Through landscape topography, we can determine the biological features and progression process of IGC. To investigate the influence of H. pylori infection on IGC, we simulated different degrees of H. pylori infection. As the H. pylori infection becomes more serious, the landscape topography changes accordingly. A fourth state, named the intestinal metaplasia (IM) state, emerges on the landscape and is associated with a very high risk of developing gastric cancer. The emergence of this state is due to the interactions/regulations among genes. Through variations in the landscape topography, we can determine the influence of H. pylori infection on IGC. Finally, we use global sensitivity analysis to research the regulations most sensitive to IGC prevention or therapies. This study presents a new approach and a novel model with which to explore the mechanism of IGC. The simulations of different degrees of H. pylori infection can provide us with a systematic view of IGC progression. The key regulations found can give us some insight and guidance for clinical trials and experimental studies.