SRA

Immunotherapy has made significant breakthroughs in the treatment of gastric cancer (GC), and it is urgent to identify and classify patients who can benefit from immunotherapy in advance. Here, 30 patients with GC were enrolled and were divided into three groups (PR, partial response; SD, stable disease; PD, progressive disease) according to efficacy evaluation. 16S rRNA sequencing were performed to analyze the gut microbiome signature of patients at three timepoints. We found that immunotherapy interventions perturbed the gut microbiota of patients. Specifically, at baseline, the abundance of Phascolarctobacterium and Desulfovibrio was relatively high, while the abundance of Bacteroides significantly increased after immunotherapy. Additionally, although differences at the enterotype level did not distinguish patients' immunotherapy response, we identified 6,7, and 19 species that were significantly enriched in PR, SD, and PD, respectively. Functional analysis showed that betalain biosynthesis and indole alkaloid biosynthesis were significantly different between the responders and non-responders. Furthermore, machine learning model based solely on bacterial biomarkers predicted immunotherapy efficacy with an Area Under the Curve (AUC) of 0.941. Among them, Akkermansia muciniphila and Dorea formicigenerans profoundly contributed to the classification of immunotherapy efficacy. In conclusion, our study reveals that gut microbiome signatures can be utilized as effective biomarkers for predicting the immunotherapy efficacy for GC.