As a marker of Helicobacter pylori, Cytotoxin-associated gene A (CagA) has been revealed to be the major virulence factor to cause gastroduodenal diseases. However, the molecular mechanisms that underlie the development of different gastroduodenal diseases caused by cagA-positive H. pylori infection remain unknown. Current studies are mainly limited to the relationship between EPIYA motifs in the CagA strain and diseases, but such a relationship is insufficient to explain the diversity of diseases. We propose a new and systematic method to analyze the relationship between the whole CagA sequence patterns and diseases. For this purpose, we introduced entropy calculation to detect key residues of CagA as the gastric cancer biomarkers, and then employed a supervised learning procedure to classify the cancer and non-cancer related CagA strains by using the key residues. We achieved 76% and 71% classification accuracy for Western and East Asian subtypes, respectively. Our study may help establish H. pylori biomarkers for predicting gastroduodenal disease outcome.
['Chao Zhang', 'Shunfu Xu', 'Dong Xu']
Detection and application of CagA sequence markers for assessing risk factor of gastric cancer caused by Helicobacter pylori