Gut microbiota and metabolite signatures predict severe immune-related adverse events in advanced hepatobiliary cancers.
TL;DR
Patients with severe irAEs exhibited unique changes in microbiota-fungi-metabolite interactions and Gut microbiota- and/or metabolite-based algorithms could be used as additional tools for predicting severe irAEs and as potential prognostic markers in advanced hepatobiliary cancers.
OpenAlex 토픽 ·
Colorectal Cancer Treatments and Studies
Cancer Immunotherapy and Biomarkers
Gut microbiota and health
Patients with severe irAEs exhibited unique changes in microbiota-fungi-metabolite interactions and Gut microbiota- and/or metabolite-based algorithms could be used as additional tools for predicting
APA
Chengpei Zhu, Dongya Zhang, et al. (2026). Gut microbiota and metabolite signatures predict severe immune-related adverse events in advanced hepatobiliary cancers.. Journal of advanced research, 83, 775-787. https://doi.org/10.1016/j.jare.2025.08.016
MLA
Chengpei Zhu, et al.. "Gut microbiota and metabolite signatures predict severe immune-related adverse events in advanced hepatobiliary cancers.." Journal of advanced research, vol. 83, 2026, pp. 775-787.
PMID
40812586
Abstract
[INTRODUCTION] Immune checkpoint inhibition (ICI) has proven to be a major breakthrough in hepatobiliary cancers treatment. However, immune-related adverse events (irAEs) remain a major concern. The gut microbiome has been implicated in ICI efficacy; however, specific alterations in the multi-kingdom gut microbiota associated with severe irAEs are not well understood.
[OBJECTIVES] We aimed to identify the signatures of gut microbiota, fungi, and metabolites in patients with advanced hepatobiliary cancers with severe irAEs compared to those in patients experiencing mild or no irAEs.
[METHODS] We enrolled 168 patients with advanced hepatobiliary cancers between June 2018 and June 2022 (72 in the train set, 31 in test set 1, and 65 in test set 2). Multi-kingdom microbiota profiles were investigated using metagenomic, ITS2, and metabolomic datasets.
[RESULTS] The presence of severe irAEs was associated with significantly longer overall survival compared with the irAE-Mild and irAE-No groups. Patients with severe irAEs showed significant differences in the composition of bacteria and metabolites, but relatively few differences in fungi, and had more complex network associations of multi-kingdom gut microbiota compared with the irAE-Mild and irAE-No groups. A predictive model based on four bacteria and six metabolites simultaneously discriminated irAE-Severe from irAE-Mild and irAE-No with high accuracy.
[CONCLUSION] Patients with severe irAEs exhibited unique changes in microbiota-fungi-metabolite interactions. Gut microbiota- and/or metabolite-based algorithms could be used as additional tools for predicting severe irAEs and as potential prognostic markers in advanced hepatobiliary cancers.
[OBJECTIVES] We aimed to identify the signatures of gut microbiota, fungi, and metabolites in patients with advanced hepatobiliary cancers with severe irAEs compared to those in patients experiencing mild or no irAEs.
[METHODS] We enrolled 168 patients with advanced hepatobiliary cancers between June 2018 and June 2022 (72 in the train set, 31 in test set 1, and 65 in test set 2). Multi-kingdom microbiota profiles were investigated using metagenomic, ITS2, and metabolomic datasets.
[RESULTS] The presence of severe irAEs was associated with significantly longer overall survival compared with the irAE-Mild and irAE-No groups. Patients with severe irAEs showed significant differences in the composition of bacteria and metabolites, but relatively few differences in fungi, and had more complex network associations of multi-kingdom gut microbiota compared with the irAE-Mild and irAE-No groups. A predictive model based on four bacteria and six metabolites simultaneously discriminated irAE-Severe from irAE-Mild and irAE-No with high accuracy.
[CONCLUSION] Patients with severe irAEs exhibited unique changes in microbiota-fungi-metabolite interactions. Gut microbiota- and/or metabolite-based algorithms could be used as additional tools for predicting severe irAEs and as potential prognostic markers in advanced hepatobiliary cancers.
MeSH Terms
Humans; Gastrointestinal Microbiome; Male; Female; Middle Aged; Aged; Liver Neoplasms; Immune Checkpoint Inhibitors; Biliary Tract Neoplasms; Metabolome; Drug-Related Side Effects and Adverse Reactions
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