Rapid profiling of carcinogenic types of Helicobacter pylori infection via deep learning analysis of label-free SERS spectra of human serum

WHO classified <i>Helicobacter pylori</i> as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of <i>H. pylori</i> infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent <i>H. pylori</i> strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that <i>H. pylori</i> infection is divided into two types in terms of the presence and absence of CagA and VacA toxins in serum, that is, carcinogenic Type I infection (CagA+/VacA+, CagA+/VacA-, CagA-/VacA+) and non-carcinogenic Type II infection (CagA-/VacA-). Currently, detecting the two carcinogenic toxins in active modes is mainly done by diagnosing their serological antibodies. However, the method is restricted by expensive reagents and intricate procedures. Therefore, establishing a rapid, accurate, and cost-effective way for serological profiling of carcinogenic <i>H. pylori</i> infection holds significant implications for effectively guiding <i>H. pylori</i> eradication and gastric cancer prevention. In this study, we developed a novel method by combining surface-enhanced Raman spectroscopy with the deep learning algorithm convolutional neural network to create a model for distinguishing between serum samples with Type I and Type II <i>H. pylori</i> infections. This method holds the potential to facilitate rapid screening of <i>H. pylori</i> infections with high risks of carcinogenesis at the population level, which can have long-term benefits in reducing gastric cancer incidence when used for guiding the eradication of <i>H. pylori</i> infections.