LAUSR

Simple Summary Gastric cancer shows an improved prognosis when diagnosed in its early stage. However, non-invasive diagnostic markers for gastric cancer known to date have poor clinical efficacies. Many studies have shown that gastric cancer patients have distinct microbial changes compared to normal subjects. In the present study, we performed metagenome analysis using body fluid samples (gastric juice, blood, and urine) to investigate the distinct microbial composition using bacteria-derived EVs from gastric cancer patients. We could build diagnostic prediction models for gastric cancer with the metagenomic data and analyzed the accuracy of models. Although further validation is required to apply these findings to real clinical practice yet, our study showed the possibility of gastric cancer diagnosis with the integration of liquid biopsy and metagenome analysis. Abstract Early detection is crucial for improving the prognosis of gastric cancer, but there are no non-invasive markers for the early diagnosis of gastric cancer in real clinical settings. Recently, bacteria-derived extracellular vesicles (EVs) emerged as new biomarker resources. We aimed to evaluate the microbial composition in gastric cancer using bacteria-derived EVs and to build a diagnostic prediction model for gastric cancer with the metagenome data. Stool, urine, and serum samples were prospectively collected from 453 subjects (gastric cancer, 181; control, 272). EV portions were extracted from the samples for metagenome analysis. Differences in microbial diversity and composition were analyzed with 16S rRNA gene profiling, using the next-generation sequencing method. Biomarkers were selected using logistic regression models based on relative abundances at the genus level. The microbial composition of healthy groups and gastric cancer patient groups was significantly different in all sample types. The compositional differences of various bacteria, based on relative abundances, were identified at the genus level. Among the diagnostic prediction models for gastric cancer, the urine-based model showed the highest performance when compared to that of stool or serum. We suggest that bacteria-derived EVs in urine can be used as novel metagenomic markers for the non-invasive diagnosis of gastric cancer by integrating the liquid biopsy method and metagenome analysis.