Integrated multiomics reveals gut microbiota-protein-metabolite alterations that regulate adverse events and responses to radio-chemo-immunotherapy in microsatellite stable rectal cancer: a prospective longitudinal study

Background The potential role of fecal samples from patients with microsatellite stable (MSS) rectal cancer (RC) receiving immunotherapy was lack for investigation. By leveraging multiomics approaches, including metagenomics, metabolomics, and metaproteomics, we aimed to identify signatures that could predict treatment efficacy and adverse events and elucidate the molecular changes associated with different responses. Methods: Fecal samples were collected from patients before and after chemotherapy and immunotherapy. Metagenomic sequencing was performed to characterize microbial composition. Metaproteomic analysis was conducted to assess the protein expression profiles, and metabolomic profiling was utilized to identify metabolic changes. Data integration and differential analysis were performed. A multiomics model was constructed using machine learning algorithms to predict treatment outcomes on the basis of these signatures. Results: Our results revealed significant alterations in the fecal microbiome, proteome, and metabolome of patients after neoadjuvant immunotherapy. Differential analysis identified a set of biomarkers, including 6 bacteria, 4 meta-proteins, and 4 metabolites, that were predictive of treatment response. The multiomics model demonstrated high accuracy in predicting treatment efficacy, with a significant correlation between the model's predictions and actual clinical outcomes. Conclusion: This study demonstrated the feasibility of using fecal multi-omics data to predict the efficacy of neoadjuvant immunotherapy in RC patients. The identified biomarkers and the multiomics model provide a novel approach for personalized medicine, potentially improving treatment strategies and patient outcomes. Further validation in larger cohorts is warranted to refine the predictive model and explore its clinical applicability.