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

Abstract

WHO classified Helicobacter pylori as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of H. pylori infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent H. pylori strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that H. pylori 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 H. pylori infection holds significant implications for effectively guiding H. pylori 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 H. pylori infections. This method holds the potential to facilitate rapid screening of H. pylori 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 H. pylori infections.

Keywords

• Helicobacter pylori
• Surface-enhanced Raman spectrometry
• Deep learning algorithm
• Gastric cancer
• Serum antibody
• Carcinogenic toxin